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Google's Tablet Through Google-Only:

2012-07-23T08:26:00.000-07:00

Google's Tablet Through Google-Only:

According to the reports from Wall Street Journal, the Google Tablet will be released this year. But more than that, the latest reports says that the Google Tablet will be available exclusively on Google-only online stores. Rumors and reports about the Google Tablet has been spreading through a couple of months. First, the rumors said that the Android tablet would be positioned to challenge Amazon's Kindle Fire. Some reports said that the tablet would be 7-inches and priced between $149 and $199. Digitizes said that the Google will be cooperating with As us to launch their tablet which will be less expensive. According to the Wall Street Journal, the tablet will be named as Google and the company will employ Samsung and As us to make the hardware. If the Google is going to release its tablet through Google-only store, then it is clear that it is going to compete with Amazon because Kindle is only available through Amazon.com.
However, it remains unclear when Google plans to launch this online store.
Google did not immediately respond to request for comment.

Diabetic Dessert Recipe - Low-Sugar Carrot Macaroons

2012-07-19T09:58:00.002-07:00

Being born to a family of type 2 diabetics has been both a curse and a blessing to me. On one hand, knowing that I am genetically predisposed to this disease is like having a gloomy cloud over my head. Sometimes when I get a regular headache or have to "go number one" a little more frequently than usual, my paranoia will start chanting the word "hyperglycemia" in my head. On the other hand, the same paranoid little voice has always pushed me to take excellent care of my health, so I'm not only diabetes-free but also very healthy. As much as I try to maintain good eating habits, however, I do have one weakness. I love, love, love, love to bake sweet treats!

I simply cannot and will not quit baking. What I can do, though, is make my desserts more healthy and diabetic-friendly. The carrot macaroon recipe I'm sharing with you today is really great for diabetics, as it is high fiber, low sugar and cholesterol-free. Yet, this doesn't mean diabetics can just munch on them all day or enjoy a whole jar of them at once. These cookies are low-sugar but not sugar-free, so portion control is still very important. How would you know how many cookies you can eat without risking a blood sugar spike? Here's an effective way to judge it: monitor your overall diet, count your daily carb intake and decide how many of these carrot cookies can fit into your diet plan without exceeding your carb and calorie allotment. I'll share the nutrition facts of this recipe at the end of the article. But now, let's start baking!

Cook Time
Prep time: 10 min
Cook time: 15 min
Ready in: 25 min
Yields: about 20 macaroons

Ingredients for Low-Sugar Carrot Macaroons

    1/2 cup carrot, shredded and cut into about one-inch pieces
    1/2 cup whole-grain granola, with no added sugar
    2/3 cup unbleached all-purpose flour
    3 tbsps light margarine, (trans fat free and cholesterol-free)
    1/3 cup brown sugar
    1 egg white, beaten until stiff
    1 tsp baking soda
    1 tsp ground cinnamon
    1/2 tsp vanilla extract
    cooking spray

Let's Talk a Little Bit about these Diabetic-Friendly Ingredients

Carrot - This bright orange vegetable is rich in dietary fiber, a vital component of a diabetic diet. Fiber decelerates sugar absorption into the bloodstream and promotes better blood-sugar control. Plus, it can also lower cholesterol and reduce the risk of heart disease, one of the most common diabetic complications.

Whole-Grain Granola - Granola usually consists of rolled oats and nuts, which makes it a high-fiber ingredient with a boost of protein and healthy fats. For this diabetic cookie recipe, try to use whole-grain granola with no added sugar, honey or dried fruits. If possible, choose a brand that is labeled "sugar-free" and "high-fiber."

Unbleached All-Purpose Flour - This brownish flour may not look as nice as white flour, but it surely is better for diabetics' health. According to the Organic Consumers Association, alloxan, a chemical that makes white flour appear so "clean" can actually harm the beta cells in the pancreas and cause the body to produce less insulin. In other words, it can put non-diabetics at risk of developing diabetes and worsen the symptoms in those who already suffer from the disease. Unbleached flour, although a high-carb ingredient, is free from harmful chemicals that can handicap your pancreas.

Egg White - As diabetics are prone to heart disease, it is important to limit the consumption of cholesterol, a type of dangerous fat that clogs the arteries and causes all kinds of cardiovascular problems. While one whole egg has about 180 milligrams of cholesterol, egg whites contain none! So for diabetics who like to eat eggs, it is wiser to leave out the yolks.

Margarine - This butter substitute has gotten a bad rap for being high in trans fat, another type of harmful fat that not only clogs the arteries but also lowers the amount of healthy fats in the body. However, not all margarines are created equal. While stick margarine is high in trans fat, soft margarine hardly contains any. Some brands even carry the label "trans fat free." Margarine manufacturers nowadays have become a lot more responsive to consumers' health concerns. By switching their main ingredient from hydrogenated vegetable oil to liquid vegetable oil, the amount of trans fat in margarine is significantly lower. For this recipe, I use Country Crock light margarine, which contains zero trans fat and zero cholesterol. Compared to a high-cholesterol ingredient like butter, it is a lot better for diabetics' heart health. Keep in mind, though, that soft margarine may not be suitable for all baking recipes. From my experience, it's great for cake, muffin and soft cookie recipes, but doesn't work so well for crispy cookies.

Cinnamon - There is another good reason for diabetics to use this famous spice other than its fantastic aroma. Recent studies have found that cinnamon can improve glucose metabolism as well as increase insulin sensitivity. Dr. Joseph Mercola, a physician and author of several health books, has recently written on the Huffington Post that only half a teaspoon of cinnamon a day has been shown to significantly lower blood sugar levels, triglycerides, and total cholesterol levels in type 2 diabetics.

Instructions - How to Make Diabetic Carrot Macaroons

    Preheat an oven to 400 degrees F.
    In a large bowl, cream margarine and sugar until light and fluffy. You may do this by hand with a whisk or a big fork, or you may use an electric hand mixer instead. If you're using the same portions of ingredients as shown above, it's not too time-consuming at all to just do it by hand. If you're planning to make a much bigger batch of cookies, however, an electric mixer will save a lot of time and energy. Why is this process important? The better you mix these ingredients together, the lighter and more delicate your macaroons will be. Failing to do so won't affect the taste of your macaroons, but the texture just won't come out as lovely.
    Add egg white and vanilla extract. Mix together until combined.
    In another bowl, sift together all-purpose flour, baking soda and ground cinnamon.
    Add the flour mixture, carrot and granola to the egg mixture. Mix together well with a fork. The consistency of your macaroon batter should be slightly dense and sticky, not too soft or too crumbly.
    Lightly grease a baking sheet with cooking spray. Drop spoonfuls of the carrot macaroon batter onto the baking sheet.
    Bake in the preheated oven for about 10 - 15 minutes or until the edges start to brown.
    Remove your carrot macaroons from the oven. Carefully transfer them from the baking sheet to a wire rack. Allow them to cool for about 10 - 20 minutes before serving or putting them into a cookie jar.

Tweaking Ideas and Serving Suggestions

Make Them Sweeter without Adding Sugar

These diabetic cookies are pretty sweet but might not be sweet enough for some. If you think the amount of natural sugar in these carrot macaroons may not be high enough to satiate your sweet tooth, you can add either some sucralose (Splenda) or stevia to the recipe. Compared to other sugar substitutes, these two have been linked to fewer potential side effects and have caused the least controversy among consumer groups. (2 teaspoons of sugar = 1 packet of sucralose / 1 packet of stevia / 4 - 6 drops of liquid stevia)

Make Them Egg-Free and Vegan

Dissolve 1 tablespoon of plain agar powder in 1 tablespoon of water. Whip up the mixture, chill it and then whip it again before using. This is a good substitute for one egg white!

Don't Make Too Many At a Time

If you're not going to share these macaroons with other people, don't make a huge batch of them. They only stay good for 2 - 4 days in a cookie jar, and up to a week in a refrigerator. After a few days, the cookies' texture tends to get a bit too soft and the carrot usually loses its nice color. It's best to bake a small batch at a time and invite others to enjoy them with you.

Smart Pairing

If you plan to have these macaroons as an afternoon snack, don't pair them with creamy cappuccino, fruit juice or smoothie. You don't need any extra fat from coffee creamer or extra sugar from fruity drinks. Instead, savor your carrot cookies with a glass of plain, non-fat or low-fat milk, or a cup of tea with no added sugar.

Puri- The tourist destination:

2012-07-19T09:57:00.001-07:00

Puri- The tourist destination:

Puri is such a wonderful tourist place and which have both values include tourist attraction as well as piligrims of
India. just read the article below to know what to se at puri ad how costly it is for visiting puri and have a brief
idea about puri tourism.

How to reach at puri:

Puri is 60 kilometer far from Bhubaneswar and Bhubaneswar is well connected with Flights from all major cities of India
as well as with railways.
If you are at Bhubaneswar then you can go to puri either by road or by railway.
If you take a a/c taxi ( Tata sumo, quails,Indigo) it will cost you around Rs 1200 .
If you go by train individual ticket may cost you Rs 15 for local trains and Rs 45 for express trains.
If you go by Taxi or own car then you have to park your car near medical square of puri and the charges is just Rs 10
and The famous Lord Jagannath temple is just 400 meter from that, also Puri municipal providing bus service to take you
from your parking spot to Temple by just charging Rs 3 per person.

What to see at Puri:

You can see world famous Lord jagannath Temple which is 1000 years old and is full with historological stories as well
as mythological stories. Also you can see the famous Puri beach which is the main attraction for International tourist.

Where to stay at puri?

If a tourist has come from outside of odisha the definitely he will be searching for lodging at puri.
Infront of puri beach most of the hotels are available, it includes budgetary hotel and economy hotel.
Room rates are start from RS 300-Rs5000. Mayfair a chain group of hotel also present there. So one can
get their accommodation as per their budget and comfort.

What to eat at puri?

At puri as tourist come from all part of India so all type of foods like Indian, Chinese, thai, and many multi cuisine foods are available also there are some specialized hotels who provide Punjabi thali, Marwadi Thali, Gujrati thali, South Indian Tiffin and lunch and many more.
The foods are also available at starting from range of RS 20 to Rs 200. So we can say that food is as cheaper than other tourist place.

Do not forget to buy some books about Lord jagannath and capture some images in around puri specially at sea beach

Different ways of 'Heat Transfer'

2012-07-19T09:56:00.001-07:00

This resource deals with the overview or some highlights of basics of heat transfer process in the real world.
There are mainly three modes of heat transfer viz heat conduction in solids, heat convection in fluid-solid interaction
and radiation from any object with finite temperature.

Introduction:

Heat transfer is the thermal process of transferring the heat from higher temperature region to lower temperature
region (due to temperature gradient). In actual practice there are main three modes via heat can be transferred.

1. Heat Conduction
2. Heat Convection
3. Thermal Radiation

Overview of Heat transfer modes like conduction convection radiation

Heat Conduction:

Conduction is the heat transfer between bodies and matters which are in direct contact. Conduction occurs at macroscopic
level between adjacent molecules of solids. Conduction happens due to diffusive transfer of kinetic energy of molecules
with adjacent molecules.

The material with higher thermal conductivity(a property of material) can rapidly absorb and reject the heat compared to
the material with lower thermal conductivity. E.g. Silver is more conductive than iron.

Conduction is governed by Fourier's Law. According to the law, heat transfer by conduction is directly proportional to
the amount of temperature difference and inversely proportional to the distance between them.

Heat conduction always requires solid body medium for heat transfer.

Heat Convection:

Convection is the transfer of the heat between solid and liquid interface due to bulk motion of particles of fluid with
solid body. Normally stationary fluid in contact with solid body transfers the heat by mainly due to conduction only not
by convection. Convection is dominant than conduction when fluid is in motion.

Heat convection is governed by 'Newton's law of cooling' which tell that amount of heat transfer by convection is
directly proportional to the temperature difference between the solid and fluid and heat transfer coefficient. Heat
transfer coefficient is dependent on geometry, flow property.

Heat transfer to cup of tea during heating on stove is an example of convection.

Heat convection requires fluid-solid interface as medium for heat transfer.

Thermal Radiation:

Thermal radiation (not radioactive radiation) is heat transfer due to finite temperature of body itself. The body emits
thermal radiation proportional to the fourth power of its temperature multiplied by Stephan Boltzmann Constant.

Thermal radiation takes in form of electromagnetic waves and it does not required any medium for heat transfer. Even in
vacuum, thermal radiation can happen. In actual practice, we feel hotter due to sunlight which is nothing but the example
of thermal radiation.

One can reduce the thermal radiation by putting radiation shield of high reflective material to add resistance to the
flow of thermal radiation. This is the only way to reduce thermal radiation. Other modes like conduction and convection can be removed by use of vacuum but thermal radiation cannot be removed completely.

Heat transfer in furnace is example of thermal radiation.

My mystery of an untold history

2012-07-19T09:35:00.000-07:00

My mystery of an untold history

I remember, it was a Saturday night. I do not remember the month. It was a rainy day. I went to my relatives home and
was returning to my home. Due to rain there was darkness in the street. I was alone with an umbrella in my hand. While
walking I found something glittering on the right side of the road. I reached there. With the help of my umbrella I
touched it and found it to be a golden ring. I took it in my hand and wore in on my right hand's ring finger. It was
fitting me. I became happy and worried. I reached home, removed the ring and placed it in my pooja room in front of the
brass lamp. I thought someone would come in search of the lost ring, if no one comes, I should hand it over to the police
station. I went to bed and rested.

The same night, someone pressed the calling bell from outer gate. My hubby was tired and was having a sound sleep.
I did not disturb him. I opened the door and asked who was that. I could see a young girl in a black saree waiting
near the outer gate. I asked her to take out the latch and come inside. She came in and said." I want to ask you
something". I asked,"Who are you?. She did not reply. I repeatedly asked her ,"who are you?" She said,"I want my ring.
You took my ring from the road side". I said," Yes. Is it your ring. Wait. I will get it". I went inside to get the ring.
To my utter surprise I did not find the ring there. I shook my husband and asked him,"Did you see a ring in the pooja
room? Did you take it? It is missing. The owner of the ring has come to take it". My hubby was blinking and said,"I don't
know any thing. What are you talking?. Then I went outside to tell the girl . I found the girl missing. I started
worrying about the girl and the disappearance of the ring. I started asking many questions in my mind about the ring
and the girl.

The next day. The call bell rang. I was afraid. I saw the same girl standing outside the gate. She started shouting
from there "Where is my ring, stupid lady". I said,"The ring disappeared". I called my husband,"Darling, here is the
girl who came yesterday". Before he could come out ,the girl disappeared.

Next day, I went to the police station and narrated the whole event to the Inspector of police. He promised to send a
police man at night. The police man was asked to stay in a near by room. But the girl did not come. The police was
informed, and the next day also police was positioned at my home. The young girl did not come. I thought the girl's
play was over. I informed the police not to come any more. The next day, the same girl came home and rang the door bell.
I saw her and called my husband , and he rushed out to see her. She vanished.

Finally we approached a Tantrik who predicts things and recommends solutions. He predicted that she was a young girl
who was molested and killed on the road and her soul is still roaming in the streets. The molesters have looted her
golden ring before killing her. Generally she plays mischief with ladies with out harming them. She does not like
youths between the age of 18 and 25 who were the merciless killers of the young girl.

He performed some rituals and offered a holy thread to be tied at the entrance to my home, to stop her coming home
in the future. I was greatly relieved after this. This is the mystery of my history.

This is a story created for creative writing contest at ISC.

Two Techniques for fast Computations

2012-03-10T04:48:00.000-08:00

A major obstacle against implementing distributed multimedia applications (e.g., web broadcasting, video teleconferencing, and remote diagnosis) is the difficulty of ensuring quality of service (QoS) over the Internet. A fundamental problem that underlies many important network function such as QoS routing, MPLS path selection, and traffic engineering, is to find the constrained shortest path—the cheapest path that satisfies a set of constraints. It is the cheapest path whose end-to-end delay is bounded by the delay requirement of a time-sensitive data flow. The additional bandwidth requirement, if there is one, can be easily handled by a pre-processing step that prunes the links without the required bandwidth from the graph. The algorithms for computing the constrained shortest paths can be used in many different circumstances, for instance, laying out virtual circuits in ATM networks, establishing wavelength-switching paths in fiber-optics networks, constructing label-switching paths in MPLS based on the QoS requirements in the service contracts, or applying together with RSVP. There are two schemes of implementing the QoS routing algorithms on routers The first scheme is to implement them as on-line algorithms that process the routing requests as they arrive. In practice, on-line algorithms are not always desired. When the request arrival rate is high (major gateways may receivethousands or tens of thousands of requests every second), even the time complexity of Dijkstra’s algorithm will overwhelm the router if it is executed on a per-request basis.1 To solve this problem, the second scheme is to extend a link-state protocol (e.g., OSPF) and periodically pre-compute the cheapest delay-constrained paths for all destinations, for instance, for voice traffic with an end-to-end delay requirement of 100 ms. The computed paths are cached for the duration before the next computation. This approach provides support for both constrained unicast and constrained multicast. The computation load on a router is independent of the request arrival rate. Moreover, many algorithms, including those we will propose shortly, have the same time complexity for computing constrained shortest paths to all destinations or to a single destination. This paper studies the second scheme. A path that satisfies the delay requirement is called a feasible path. Finding the cheapest (least-cost) feasible path is NP-complete. There has been considerable work in designing heuristic solutions for this problem. Xue [12] and

Juttner et al. [13] used the Lagrange relaxation method to approximate the delay-constrained least-cost routing problem. However, there is no theoretical bound on how large the cost of the found path can be. Korkmaz and Krunz used a nonlinear target function to approximate the multi-constrained least-cost path problem [14]. It was proved that the path that minimizes the target function satisfies one constraint and the other constraints multiplied by , where is a predefined constant and is the number of constraints. However, no known algorithm can find such a path in polynomial time. Ref. [14] proposed a heuristic algorithm, which has the same time complexity as Dijkstra’s algorithm. It does not provide a theoretical bound on the property of the returned path, nor provide conditional guarantee in finding a feasible path when one exists. In addition, because the construction of the algorithm ties to a particular destination, it is not suitable for computing constrained paths from one source to all destinations. For this task, it is slower than the algorithms proposed in this paper by two orders of magnitude based on our simulations.

Existing System:

Existing system uses RTF and RTC built up for discretization error along a path.
Here efficiency of the algorithms directly relates to the magnitude

of the errors introduced during discretization .

Proposed System:

In our project we use two techniques to decrease the discretion error.

Here we use randomized discretization and path delay discretization techniques.

· The above new techniques either make the link errors to cancel out each other along the path or treat the path delay as a whole for discretization, which results in much smaller errors.

The algorithms based on these techniques run much faster than the best existing algorithm

Technique used or algorithm used:

RANDOMIZED DISCRETIZATION
PATH DELAY DISCRETIZATION

Advantages:

Shortest path is found using time.
Shortest path varies according to the congestion occurred in the path.

1.2 SYSTEM ANALYSIS

After analyzing the requirements of the task to be performed, the next step is to analyze the problem and understand its context. The first activity in the phase is studying the existing system and other is to understand the requirements and domain of the new system. Both the activities are equally important, but the first activity serves as a basis of giving the functional specifications and then successful design of the proposed system. Understanding the properties and requirements of a new system is more difficult and requires creative thinking and understanding of existing running system is also difficult, improper understanding of present system can lead diversion from solution.

ANALYSIS MODEL

SDLC METHDOLOGIES

This document play a vital role in the development of life cycle (SDLC) as it describes the complete requirement of the system. It means for use by developers and will be the basic during testing phase. Any changes made to the requirements in the future will have to go through formal change approval process.

SPIRAL MODEL

It was defined by Barry Boehm in his 1988 article, “A spiral Model of Software Development and Enhancement. This model was not the first model to discuss iterative development, but it was the first model to explain why the iteration models.

As originally envisioned, the iterations were typically 6 months to 2 years long. Each phase starts with a design goal and ends with a client reviewing the progress thus far. Analysis and engineering efforts are applied at each phase of the project, with an eye toward the end goal of the project.

The steps for Spiral Model can be generalized as follows:

· The new system requirements are defined in as much details as possible. This usually involves interviewing a number of users representing all the external or internal users and other aspects of the existing system.

· A preliminary design is created for the new system.

· A first prototype of the new system is constructed from the preliminary design. This is usually a scaled-down system, and represents an approximation of the characteristics of the final product.

· A second prototype is evolved by a fourfold procedure:

1. Evaluating the first prototype in terms of its strengths, weakness, and risks.

2. Defining the requirements of the second prototype.

3. Planning a designing the second prototype.

4. Constructing and testing the second prototype.

· At the customer option, the entire project can be aborted if the risk is deemed too great. Risk factors might involve development cost overruns, operating-cost miscalculation, or any other factor that could, in the customer’s judgment, result in a less-than-satisfactory final product.

· The existing prototype is evaluated in the same manner as was the previous prototype, and if necessary, another prototype is developed from it according to the fourfold procedure outlined above.

· The preceding steps are iterated until the customer is satisfied that the refined prototype represents the final product desired.

· The final system is constructed, based on the refined prototype.

· The final system is thoroughly evaluated and tested.Routine maintenance is carried on a continuing basis to prevent large scale failures and to minimize down time.

The following diagram shows how a spiral model acts like:

Fig 2.1 Spiral Model

STUDY OF THE SYSTEM

In the flexibility of the uses the interface has been developed a graphics concept in mind, associated through a browser interface. The GUI’S at the top level have been categorized as

Administrative user interface

1.The operational or generic user interface

The administrative user interface concentrates on the consistent information that is practically, part of the organizational activities and which needs proper authentication for the data collection. The interfaces help the administrations with all the transactional states like Data insertion, Data deletion and Data updating along with the extensive data search capabilities.

The operational or generic user interface helps the users upon the system in transactions through the existing data and required services. The operational user interface also helps the ordinary users in managing their own information helps the ordinary users in managing their own information in a customized manner as per the assisted flexibilities

LITERATURE SURVEY

Literature Survey:

Approximation algorithms

In computer science and operations research, approximation algorithms are algorithms used to find approximate solutions to optimization problems. Approximation algorithms are often associated with NP-hard problems; since it is unlikely that there can ever be efficient polynomial time exact algorithms solving NP-hard problems, one settles for polynomial time sub-optimal solutions. Unlike heuristics, which usually only find reasonably good solutions reasonably fast, one wants provable solution quality and provable run time bounds. Ideally, the approximation is optimal up to a small constant factor (for instance within 5% of the optimal solution). Approximation algorithms are increasingly being used for problems where exact polynomial-time algorithms are known but are too expensive due to the input size. A typical example for an approximation algorithm is the one for vertex cover in graphs: find an uncovered edge and add both endpoints to the vertex cover, until none remain. It is clear that the resulting cover is at most twice as large as the optimal one. This is a constant factor approximation algorithm with a factor of 2. NP-hard problems vary greatly in their approximability; some, such as the bin packing problem, can be approximated within any factor greater than 1 (such a family of approximation algorithms is often called a polynomial time approximation scheme or PTAS). Others are impossible to approximate within any constant, or even polynomial factor unless P = NP, such as the maximum clique problem. NP-hard problems can often be expressed as integer programs (IP) and solved exactly in exponential time. Many approximation algorithms emerge from the linear programming relaxation of the integer program. Not all approximation algorithms are suitable for all practical applications. They often use IP/LP/Semidefinite solvers, complex data structures or sophisticated algorithmic techniques which lead to difficult implementation problems. Also, some approximation algorithms have impractical running times even though they are polynomial time, for example O(n²⁰⁰⁰). Yet the study of even very expensive algorithms is not a completely theoretical pursuit as they can yield valuable insights. A classic example is the initial PTAS for Euclidean TSP due to Sanjeev Arora which had prohibitive running time, yet within a year, Arora refined the ideas into a linear time algorithm. Such algorithms are also worthwhile in some applications where the running times and cost can be justified e.g. computational biology, financial engineering, transportation planning, and inventory management. In such scenarios, they must compete with the corresponding direct IP formulations. Another limitation of the approach is that it applies only to optimization problems and not to "pure" decision problems like satisfiability, although it is often possible to conceive optimization versions of such problems, such as the maximum satisfiability problem. Inapproximability has been a fruitful area of research in computational complexity theory since the 1990 result of Feige, Goldwasser, Lovasz, Safra and Szegedy on the inapproximability of Independent Set. After Arora et al. proved the PCP theorem a year later, it has now been shown that Johnson's 1974 approximation algorithms for Max SAT, Set Cover, Independent Set and Coloring all achieve the optimal approximation ratio, assuming P != NP.

QoS

In the field of computer networking and other packet-switched telecommunication networks, the traffic engineering term quality of service (QoS) refers to resource reservation control mechanisms rather than the achieved service quality. Quality of service is the ability to provide different priority to different applications, users, or data flows, or to guarantee a certain level of performance to a data flow. For example, a required bit rate, delay, jitter, packet dropping probability and/or bit error rate may be guaranteed. Quality of service guarantees are important if the network capacity is insufficient, especially for real-time streaming multimedia applications such as voice over IP, online games and IP-TV, since these often require fixed bit rate and are delay sensitive, and in networks where the capacity is a limited resource, for example in cellular data communication. In the absence of network congestion, QoS mechanisms are not required. A network or protocol that supports QoS may agree on a traffic contract with the application software and reserve capacity in the network nodes, for example during a session establishment phase. During the session it may monitor the achieved level of performance, for example the data rate and delay, and dynamically control scheduling priorities in the network nodes. It may release the reserved capacity during a tear down phase. A best-effort network or service does not support quality of service. An alternative to complex QoS control mechanisms is to provide high quality communication over a best-effort network by over-provisioning the capacity so that it is sufficient for the expected peak traffic load. In the field of telephony, quality of service was defined in the ITU standard X.902 as "A set of quality requirements on the collective behavior of one or more objects". Quality of Service comprises requirements on all the aspects of a connection, such as service response time, loss, signal-to-noise ratio, cross-talk, echo, interrupts, frequency response, loudness levels, and so on. A subset of telephony QoS is Grade of Service (GOS) requirements, which comprises aspects of a connection relating to capacity and coverage of a network, for example guaranteed maximum blocking probability and outage probability. QoS is sometimes used as a quality measure, with many alternative definitions, rather than referring to the ability to reserve resources. Quality of service sometimes refers to the level of quality of service, i.e. the guaranteed service quality. High QoS is often confused with a high level of performance or achieved service quality, for example high bit rate, low latency and low bit error probability. See also Relation to subjective quality measures below.

Delay Estimation

Introduction to the Non-Parametric Estimation of Delay Distribution

The goal of this work is to show how an end-to-end measurement can be used to infer per-link delay in a network. Special attention will be paid to the estimation of the probability distribution of per-link variable delay. The strategic direction is to define a logical model for the physical network, which is called the Tree model.

The focus of the estimate is not the physical propagation delay, because, usually, it does not influence the behavior of the network in a crucial way and is a more manageable value than the additional variable delay attributable to queuing in buffers or other processing in a router.

The inference strategy is aimed at the estimation of the variable delay previously mentioned and is extended from an estimate of end-to-end delays obtained by end-to-end measurements to the events of interest inside the network, such as per-link delays. Knowing these quantities, it is possible to define the delay distribution for each link by using only the measured distributions of end-to-end delay of the multicast or unicast packets.

The next section describes a logical model for studying a network topology, which is called the Tree model. A physical network is replaced by a logical tree composed of a root and the branch nodes that get down from it to the leaf receivers nodes. The root is the source, which sends the packets to a set of receivers, and the end-to-end delay is a measurement of a path from the root to a leaf receiver.

The research of distribution delay of an internal node delay is very complex. In fact, it is obtained by analyzing the different ways in which the end-to-end delay can be split between the portion of the path above or below the node in question. The key assumption is that the per-link delays between different links and packets should be considered independently. The packets are potential subject to queuing and loss over each link. That is why the probability distribution should be estimated along each link.

Due to the fact that the distribution of a link delay in the network is unknown, the characterization of the variable delay is obtained by non-parametric discrete Distributions. It also allows to obtain a broad range of different delay distributions. The model is a discrete model, because the result of the inference is a discrete probability distribution. A discretization of continuous time in a slotted time is made. Each slot consists of a bin time, which can be either fixed or variable. This approach allows to obtain the inference by simply using the algebraic computations and provides the balance between the accuracy of the distribution and the cost of calculation. The cost is inversely proportional to the bin width of the discrete distribution. The model decreases continuously until the null the width of the bin. The application of the inverse Laplace transforms makes the continuous model feasible.

In the following section 3.3 there will be described the algorithm of estimating the per-link discrete distributions by using the measured end-to-end delay distributions only. The model is based on the definition of the tree model and the likelihood function, described in the Section 2.6, under the key assumption of independent delay over each link.

The Tree Model

The tree model represents a physical network as a graph . denotes the physical nodes such as routers or switches, and defines the link between them. A source sender probe is called a root and is labeled as 0Î . A set of receivers is denoted as RÌ . The tree model is defined by the set of paths from the root to each rÎ R and forms a tree Tphys in .

The tree model is defined as a binary tree, where there is no possibility for two diverged paths to intersect one more time. It is possible to move from a physical model to a logical model, which is more simple to manage and takes into consideration all the characteristics of the physical one. A logical source tree can be defined T=(V,L).

Figure 3.1: A logical tree. An ancestor j’>k’ and the first common ancestor are defined. f(k’) represents the parent of k’.

Delay model

Delay model [10] is the method to define a delay over the links or a path in a tree model. The node probe is the root. Let <i,j> be a packet pair sent to destination i and j respectively. A packet pair can be described as a train consisting of two carriages, one of which goes behind the other. They cover a common path till a branch node and are directed to nodes i and j. The common path is the sequence of a set of links down to node i v j. Let us denote p(i,j) a sequence of links traversed by at least one of the two members of the packet pair. Let kÎp(i,j) be, and denote S(k)Í{1,2} as the set of packets which across k, where 1 and 2 are two members of packet pairs sent to i and j. , with is observed, and it represents the cumulated delay along the root to node k.

A measurement represents the end-to-end delay from the root to the end receivers i and j. expresses this couple of one way delay. Where is the delay from the root to the destination i for the first member and is the delay from the root to the destination j for the second member. This is the only measurement which can be used and it takes into consideration the definition of the tomography with the active external measurement (see Section 2).

It is also possible to apply a delay model to the packet pairs. Each member of a packet pair traverses a common path to arrive to the respective destination. This common path is vital for the delay model. Let and be a pair of random variable for each node k. and represents the estimated value of delay over a link (f(k),k)ÎL for the first member and for the second one of the packet pair. They can have values in the real line È¥. , because a delay cannot be negative. The value ¥ can be assumed if the packet is dropped on a link and is not able to reach the address receiver. For the hypothesis = equals to 0. Two kinds of independence are required for this model: for the delay between different pairs and for the delay within the same pair but over different links. For kÎV, the difference between the delay experienced by the first and the second member of pair crossing k. is a quantity which measures how large is the cumulated delay between two members in k.

Another hypothesis is to consider =0. This is a rough approximation. The practical application shows a different delay and ¹0. The state of the network can be not stationary. When a packet is sent, it can meet different states of the network, because it is time-dependent. The first and the second member test the network in different time because they are distanced even if the temporal gap between them is small. For example, a bottleneck can have a different impact on the first and the second member of a packet pair. can never have a null value, even if there is a perfect back-to-back packets, as, for

example, in case of a low traffic state of a network. The second member must wait the transmission of the first one. For this reason there is always a delay which is impossible to avoid.

The goal of the root is to send the pair packets. An experiment consists in sending n packets pairs <i,j> for each pair of distinct receivers i, j ÎR . Let us denote the set of measurements of this experiment by :

where

It is the delay experienced by the m-th packet pair <i,j>. To define the complete set of measurements means to group all the set of measurement for each end receivers i,jÎR

Figure 3.2 shows how the set of measurements and the complete set of measurements X are computed.

Figure 3.2: A m-th packet pair is sent to end receiver i and j. The set of measurement for m=1 to n defines . The complete set of measurement X is obtained combining all possible pair of distinct receivers i,j .

The delay analysis

The random variable can have infinite values. Some of these values have more probability than the others to be measured. The main idea is to quantify the real line È¥ as a finite set of possible delay Q. It is possible to group many similar delays in a unique interval. It enables to characterize the model as a discrete model. The estimation of will represent the probability distribution of these intervals. There are, in fact, some values which have more probability to be estimated and to belong to an interval than others. This model tries to estimate the discrete version of a continuous probability distribution. Actually, without a quantification, the probability distribution of is defined by an infinite number of value. Let be the distribution of , where

and to obtain a which is the set of distribution for each link k ÎV .

The Figure 3.3 shows a possible probability distribution of delay over link k.

Figure 3.3: Example of probability distribution delay over link k.

Fixed Bin Size Discrete Model

Let Q be a set of finite delays. Delays are discretized to Q and takes a value in Q. Fixed bin size discrete model defines the set Q as

where q is a fixed bin size chosen a priori. The accuracy of the discretization depends on the choice of q. A smaller bin size provides more accuracy to the estimation of the probability distribution of . The continuous model is a case of discrete model with infinite bins obtained with . The set Q defined in the Equation 3.6 is a finite set of values where Bq represents the greatest delay of them. The point ¥ expresses the case of packet dropped. Let us denote for each iqÎQ the interval of q-th bin as

i=1,..,B

and associate the bin to the value ¥ for the case of packet lost

and for the case of 0, while the delay cannot have a negative value

Figure 3.4: Each interval assigns a unique value to a set of values within it.

Each value contained in i-th interval will have a unique value iq. This model introduces an error of quantization . Fixed bin size discrete model is named (q,B) model for the structure of the set Q.

The estimate of a (Equation 3.5) is the goal of inference problem. It can be obtained by using the maximum likelihood approach. The set of measurements X defines the likelihood function. It is discretized to the set Q, therefore, likelihood function is a discrete function. A measurement represents two one-way delays of the first and the second members of a packet pair sent (Equation 3.2).

Figure 3.5 shows how this discretization is obtained from a continuous time.

The bidimensional discretization allows to define the space of measurement W. Let W=QxQ be the space of the possible values taken by the measurements after the discretization of the set Q. For each pair of receivers i,j it is possible to define a m-th measurement = ÎW. For m=1 to n packets pair <i, j> sent a collection of measurements is made. The Figure 3.6 shows the discrete space W . It is important to observe that the measurements are made only when the end receivers have been chosen.

Let us denote the number of packet pairs, for which = , by . It represents a bidimensional histogram on W space. It depends, in fact, on the time from m=1 to n = .

Let us denote the maximum likelihood function of the measurement X by lik(X;a). Using the Equation 3.10, let L(X;a) be the log-likelihood of the measurement X.

To estimate a by using MLE, it is necessary to maximize the Equation 3.11. If the set of measurements is obtained, a function can be maximized depending on a only

The use of the Equation 3.11 does not provide a direct expression for . A more complex approach [11,12] can be used in the Equation 3.11 applying the Expectation Maximum algorithm. This algorithm allows to iteratively obtain a, step by step researching a local maximum of the likelihood function. Let us denote the value of a at l-th step. The algorithm works until the estimated value , reaches a stationary solution.

A stationary solution is a local point of maximum of the function where the algorithm reaches the steady state . L represents the necessary steps to get the stationary solution.

The problem is that is an unknown value, which makes this approach infeasible. How can be computed if it is to infer d and its probability is to be estimated? It can be done by estimating the maximum of Equation 3.17 using the Expectation Maximum algorithm.

The EM algorithm

The Expectation Maximum algorithm [11] is used to find the local maximum point of a function. In this inference problem, the function is the maximum likelihood defined by the Equation 3.15. The unknown quantity to estimate is the delay probability distribution a. The EM algorithm adopts a dynamic analysis of the function. The research consists of many steps to obtain the exact maximum. The EM represents a model which uses the history of its research to infer the maximum in the present.

Let ranging from l=0,1,.., to a local maximization of the likelihood function be the iterative solution. l represents the l-th step of the research.

Knowing in the Equation 3.18, the delay distribution over link (f(k),k) with kÎV can be estimated. The inference target moves, therefore, to research and then, consequently, to obtain . The EM algorithm requires the complete data log-likelihood to conduct the research.

1. Initialization. Select the initial delay distribution . This is the choice of the starting point of the EM algorithm. can be assumed as an estimate of the approach [6].

2. Expectation. Compute the conditional expectation of the log-likelihood. The quantities known are the complete set of measurement X and the current estimate . Let a’ be the probability distribution delay in this expectation.

(3.19)

where the conditional expectation can be computed as

The Equation 3.20 is equivalent to the Equation 3.17. It differs only by the number which replaces . This is the advantage, because this number can be estimated and adopted to define the likelihood function and its maximum.

The goal is to estimate the numbers by their conditional expectation under the probability law induced by with the complete knowledge of the measurements X. The computation of is equal to calculating how many times ,"i,jÎR.

The conditional probability in the Equation 3.24 is not easy to calculate. It represents, i, j end receivers and their measurements being fixed, the probability distribution of D =d over the link k. It is not a static probability, since it is calculated under the law induced by . The theorem of Bayes makes the probability in the Equation 3.25 more clear

where L represents the terminal number of iterations.

Properties of EM algorithm

The EM algorithm is an important tool to solve the maximization of an inference problem. It is essential now to analyze its performance and define the degree of reliability of its results.

It is possible to estimate the quality of EM algorithm in terms of convergence and complexity.

Convergence represents the capability of EM algorithm to find the correct maximum point. The iterative analysis of converges to a station y point a* of the likelihood function. The stationary characteristic is defined as a asymptotic property of EM algorithm [13]. The transitory time is required to reach the steady state of the solution, if it exists. This means that if the maximum is found, it satisfies

The likelihood function L(X,D;a) can have multiple stationary points, but only one of them can be the absolute maximum.

The EM algorithm could not converge to absolute maximum but only to a local maximum. There are no rules to define the conditions to reach a unique and absolute point. That is why usually converges to a stationary local point, but not necessary the absolute one. The choice of the initial conditions plays an important role in obtaining a stationary estimate. The convergence depends, in fact, on the initial distribution of a. If is far from a local point of maximum, the burden of research will be more heavy in terms of time and computation. In the worst case, a maximum cannot be reached. It is important to initialize the EM algorithm with a specific choice, which was described in the Section 2.

The complexity of EM algorithm represents the computational burden of , which requires time roughly equal to [10]. The term n represents the number of packet pairs sent, p is an average number of the links between the root and the set of end receivers R, and, finally, B is the maximum interval of Q. To obtain the value of p the algorithm [14] should be applied.

The Choice of the Bin Size and the Initial Probability Distribution

The discretization introduces an inevitable quantization error. This error is function of the bin width. The Figure 3.5 shows how this error can change with the changing of the bin size. For a smaller bin the error is minor, because the original function can be recognized by the discretized function. For this reason the quality of the estimate depends on the choice of the bin size q. However, the increase in the accuracy of the estimate imposes higher computation costs. It is necessary to establish a trade-off between these two quantities. A smaller q provides better accuracy, but increases the computational cost. The complexity can be estimated as , the product qB being constant, and shows how it decreases when the discretization adopts a larger bin size q. The estimate was not able to capture the right accurate delay distribution.

Another important aspect is to establish when the condition is met. It is necessary to choose a bin size which not too little. In this case, , the difference between the first and the second members of packet pair, cannot be null.

The choice of the starting delay distribution plays a very important role in obtaining the local maximum point of the likelihood function. The computation of is very complex and it is described in [6].

Let , kÎV be the first member of the packet pair that arrives to the node k in a time d. represents the probability that . For each pair of end receivers i, j ÎR(k) the variable of can be obtained from a distribution of the measurement by solving a system of polynomial equations. The tree model defines f(k) as the parent of k. represents the delay between f(k) and k and is the variable to estimate. While , it is possible to define if the distributions of and are known. In fact, their deconvolution provides the distribution of if the hypothesis of independence of and , is met. This distribution will be the initial distribution for the iterative EM algorithm. The results in [6] show that

Data Flow Diagram

Node1

Node3

Node2

Node4

Node5

DESIGN

Data Flow Diagram

Node1

Node3

Node2

Node4

Node5

OBJECT ORIENTED ANALYSIS AND DESIGN:

A large number of libraries with ready-made objects for UML diagrams and task-oriented templates let you create UML diagrams of any complexity without effort.

Activity diagram
Use case diagram
Sequence diagram
Collaboration diagram
State chart diagram
Class diagram
Component diagram
Deployment diagram

UML (Unified Modeling Language) is a general-purpose modeling language used to represent the structure of complex software in a visual form, and employed in software engineering. UML diagrams are also efficient for documenting complex computer systems and software. Such visual models facilitate communication between the customer, system analysts and programmers, who write the source code. In addition, it's much easier for new programmers to understand the source code when a detailed UML diagram of it is available. Previously, when a programmer resigned a part of his work was gone with him because the code-creating process took place exclusively in his head. Now a newcomer can understand and get familiar with someone else's code without much trouble.

Programming languages may use operations and methods which are basically same, though vary by names and graphical notations. The UML language defines standards not only for operations and methods of programming languages, but also for their terminology. So, using UML diagrams for visual modeling will help you to improve the final software products, simplify the process of its creation and meet the deadlines.

Usecase diagram

sequence diagram

Activity diagram

IMPLEMENTATION

FUNCTIONAL DESCRIPTION

The entire project has been divided into four modules, they are

Design of LAN structure
Finding all possible path
Time calculation
Random discrimination

Module Description:

Design of LAN structure:

Here we design the structure for which the shortest path is to find .the structure to which we find the shortest path is drawn be

In the data flow diagram.

Finding all possible paths:

Consider a network, where is a set of nodes and is a set of directed links connecting the nodes. The delay and the cost of a link are denoted as and , respectively. The delay and the cost of a path are denoted as and , respectively. and. Let be the length (number of hops) of , and be the length of the longest path in the network. Given a delay requirement, is called a feasible path if . Given a source node, let be the set of nodes to which there exist feasible paths from . For any , the cheapest feasible path from to is defined as The delay-constrained least-cost routing problem (DCLC) is to find the cheapest feasible paths from to all nodes in which is NP-complete [19]. However, if the link delays are all integers and the delay requirement is bounded by an integer , the problem can be solved in time by Joksch’s dynamic programming algorithm [20] or the extended Dijkstra’s algorithm [17].Joksch’s algorithm is described as follows. , let be a variable storing the cost of the cheapest path from to with , and storing the last link of the path. Initially,and .NIL. Assuming that all link delays are positive, the dynamic programming is given below. Now suppose the link delays are allowed to be zero. We need to add one more step. Let be the sub graph consisting of all zero-delay links. For each , immediately after Joksch’s algorithm calculates , Dijkstra’s algorithm is executed on to improve on zero-delay paths [18].The above polynomials solvable special case with integer delays points out a heuristic solution for the general NP-complete problem with arbitrary delays. The idea is to discretize (scale and then round) arbitrary link delays to integers [15], [17], [18], [21]. There are two existing discrimination approaches, round to ceiling [17] and round to floor [18]. Both approaches map the delay requirement to a selected integer , and then discretize the link delays as follows.

Time calculation

RTC creates negative rounding errors on links. The error accumulates along a path. The accumulated error is proportional to the path length. The larger the topology, the longer a path, the larger the accumulated error. The same thing is true for RTF,which has positive rounding errors on links. In order to achieve -approximation, the accumulated error on a path cannot be too large. To reduce the error on a path, the existing algorithms based on RTC or RTF must reduce the discrimination errors on the links by using a large value. Given the time complexity proportion to .The insight is that if we can reduce the error introduced by discretization without using a larger , we can improve the performance of the algorithm.We develop two new techniques. The first one is called randomized discretization. It rounds to ceiling or to floor according to certain probabilities. The idea is for some links to have positive errors and some links to have negative errors. Positive errors and negative errors cancel out one another along a path in such a way that the accumulated error is minimized statistically. We will prove that, when the following discretization approach is used, the mean of the accumulated error on a path is zero and the standard deviation is bounded . In comparison, the mean of the accumulated error is for RTC and for RTF.

SPECIFICATION REQUIREMENTS

Hardware Requirements:

PROCESSOR : PENTIUM IV Processor

RAM : 512 MB RAM

HARD DISK : 20 GB

Software Requirements:

FRONT END : SWINGS, JFRAMEBUILDER.

OPERATING SYSTEM : Window’s Xp

BACK END : Sql Server 2005

ABOUT THE SOFTWARE

JAVA

Java was designed to meet all the real world requirements with its key features, which are explained in the following paragraph.

SIMPLE AND POWERFUL

Java was designed to be easy for the professional programmer to learn and use efficiently. Java makes itself simple by not having surprising features. Since it exposes the inner working of a machine, the programmer can perform his desired actions without fear. Unlike other programming systems that provide dozens of complicated ways to perform a simple task, Java provides a small number of clear ways to achieve a given task.

SECURE

Today everyone is worried about safety and security. People feel that conducting commerce over the Internet is a safe as printing the credit card number on the first page of a Newspaper. Threatening of viruses and system hackers also exists. To overcome all these fears java has safety and security as its key design principle.

Using Java Compatible Browser, anyone can safely download java applets without the fear of viral infection or malicious intent. Java achieves this protection by confining a java program to the java execution environment and by making it inaccessible to other parts of the computer. We can download applets with confidence that no harm will be done and no security will be breached.

PORTABLE

In java, the same mechanism that gives security also helps in portability. Many types of computers and operating systems are in use throughout the world and are connected to the internet. For downloading programs through different platforms connected to the internet, some portable, executable code is needed. Java’s answer to these problems is its well designed architecture.

OBJECT-ORIENTED

Java was designed to be source-code compatible with any other language. Java team gave a clean, usable, realistic approach to objects. The object model in java is simple and easy to extend, while simple types, such as integers, are kept as high-performance non -objects.

DYNAMIC

Java programs carry with them extensive amounts of run-time information that is used to verify and resolve accesses to objects at run-time. Using this concept it is possible to dynamically link code. Dynamic property of java adds strength to the applet environment, in which small fragments of byte code may be dynamically updated on a running system.

NEWLY ADDDED FEATURES IN JAVA 2

SWING is a set of user interface components that is entirely implemented in java the user can use a look and feel that is either specific to a particular operating system or uniform across operating systems.

Collections are a group of objects. Java provides several types of collection, such as linked lists, dynamic arrays, and hash tables, for our use. Collections offer a new way to solve several common programming problems.

Various tools such as javac, java and javadoc have been enhanced. Debugger and profiler interfaces for the JVM are available.

Performance improvements have been made in several areas. A JUST-IN-TIME (JIT) compiler is included in the JDK.

Digital certificates provide a mechanism to establish the identity of a user, which can be referred as electronic passports.

Various security tools are available that enable the user to create the user to create and store cryptographic keys ad digital certificates, sign Java Archive(JAR) files, and check the signature of a JAR file.

SWING:

Swing components facilitate efficient graphical user interface (GUI) development. These components are a collection of lightweight visual components. Swing components contain a replacement for the heavyweight AWT components as well as complex user interface components such as Trees and Tables.

Swing components contain a pluggable look and feel (PL & F). This allows all applications to run with the native look and feel on different platforms. PL & F allows applications to have the same behaviour on various platforms. JFC contains operating system neutral look and feel. Swing components do not contain peers. Swing components allow mixing AWT heavyweight and Swing lightweight components in an application.

The major difference between lightweight and heavyweight components is that lightweight components can have transparent pixels while heavyweight components are always opaque. Lightweight components can be non-rectangular while heavyweight components are always rectangular.

Swing components are JavaBean compliant. This allows components to be used easily in a Bean aware application building program. The root of the majority of the Swing hierarchy is the JComponent class. This class is an extension of the AWT Container class.

Swing components comprise of a large percentage of the JFC release. The Swing component toolkit consists of over 250 pure Java classes and 75 Interfaces contained in about 10 Packages. They are used to build lightweight user interfaces. Swing consists of User Interface (UI) classes and non- User Interface classes. The non-User Interface classes provide services and other operations for the UI classes.

Swing offers a number of advantages, which include

· Wide variety of Components

· Pluggable Look and Feel

· MVC Architecture

· Keystroke Handling

· Action Objects

· Nested Containers

· Virtual Desktops

· Compound Borders

· Customized Dialogues

· Standard Dialog Classes

· Structured Table and Tree Components

· Powerful Text Manipulation

· Generic Undo Capabilities

· Accessibility Support

SQL SERVER -2005

A database management, or DBMS, gives the user access to their data and helps them transform the data into information. Such database management systems include dBase, paradox, IMS, SQL Server and SQL Server. These systems allow users to create, update and extract information from their database.

A database is a structured collection of data. Data refers to the characteristics of people, things and events. SQL Server stores each data item in its own fields. In SQL Server, the fields relating to a particular person, thing or event are bundled together to form a single complete unit of data, called a record (it can also be referred to as raw or an occurrence). Each record is made up of a number of fields. No two fields in a record can have the same field name.

During an SQL Server Database design project, the analysis of your business needs identifies all the fields or attributes of interest. If your business needs change over time, you define any additional fields or change the definition of existing fields.

SQL SERVER TABLES

SQL Server stores records relating to each other in a table. Different tables are created for the various groups of information. Related tables are grouped together to form a database.

PRIMARY KEY

Every table in SQL Server has a field or a combination of fields that uniquely identifies each record in the table. The Unique identifier is called the Primary Key, or simply the Key. The primary key provides the means to distinguish one record from all other in a table. It allows the user and the database system to identify, locate and refer to one particular record in the database.

RELATIONAL DATABASE

Sometimes all the information of interest to a business operation can be stored in one table. SQL Server makes it very easy to link the data in multiple tables. Matching an employee to the department in which they work is one example. This is what makes SQL Server a relational database management system, or RDBMS. It stores data in two or more tables and enables you to define relationships between the tables and enables you to define relationships between the tables.

FOREIGN KEY

When a field is one table matches the primary key of another field is referred to as a foreign key. A foreign key is a field or a group of fields in one table whose values match those of the primary key of another table.

REFERENTIAL INTEGRITY

Not only does SQL Server allow you to link multiple tables, it also maintains consistency between them. Ensuring that the data among related tables is correctly matched is referred to as maintaining referential integrity.

DATA ABSTRACTION

A major purpose of a database system is to provide users with an abstract view of the data. This system hides certain details of how the data is stored and maintained. Data abstraction is divided into three levels.

Physical level: This is the lowest level of abstraction at which one describes how the data are actually stored.

Conceptual Level: At this level of database abstraction all the attributed and what data are actually stored is described and entries and relationship among them.

View level: This is the highest level of abstraction at which one describes only part of the database.

ADVANTAGES OF RDBMS

· Redundancy can be avoided

· Inconsistency can be eliminated

· Data can be Shared

· Standards can be enforced

· Security restrictions can be applied

· Integrity can be maintained

· Conflicting requirements can be balanced

· Data independence can be achieved.

DISADVANTAGES OF DBMS

A significant disadvantage of the DBMS system is cost. In addition to the cost of purchasing of developing the software, the hardware has to be upgraded to allow for the extensive programs and the workspace required for their execution and storage. While centralization reduces duplication, the lack of duplication requires that the database be adequately backed up so that in case of failure the data can be recovered.

FEATURES OF SQL SERVER (RDBMS)

SQL SERVER is one of the leading database management systems (DBMS) because it is the only Database that meets the uncompromising requirements of today’s most demanding information systems. From complex decision support systems (DSS) to the most rigorous online transaction processing (OLTP) application, even application that require simultaneous DSS and OLTP access to the same critical data, SQL Server leads the industry in both performance and capability.

SQL SERVER is a truly portable, distributed, and open DBMS that delivers unmatched performance, continuous operation and support for every database.

SQL SERVER RDBMS is high performance fault tolerant DBMS which is specially designed for online transactions processing and for handling large database application.

SQL SERVER with transactions processing option offers two features which contribute to very high level of transaction processing throughput, which are

The row level locks manager

ENTERPRISE WIDE DATA SHARING

The unrivaled portability and connectivity of the SQL SERVER DBMS enables all the systems in the organization to be linked into a singular, integrated computing resource.

PORTABILITY

SQL SERVER is fully portable to more than 80 distinct hardware and operating systems platforms, including UNIX, MSDOS, OS/2, Macintosh and dozens of proprietary platforms. This portability gives complete freedom to choose the database server platform that meets the system requirements.

OPEN SYSTEMS

SQL SERVER offers a leading implementation of industry –standard SQL. SQL Server’s open architecture integrates SQL SERVER and non –SQL SERVER DBMS with industry’s most comprehensive collection of tools, application, and third party software products SQL Server’s Open architecture provides transparent access to data from other relational database and even non-relational database.

DISTRIBUTED DATA SHARING

SQL Server’s networking and distributed database capabilities to access data stored on remote server with the same ease as if the information was stored on a single local computer. A single SQL statement can access data at multiple sites. You can store data where system requirements such as performance, security or availability dictate.

UNMATCHED PERFORMANCE

The most advanced architecture in the industry allows the SQL SERVER DBMS to deliver unmatched performance.

SOPHISTICATED CONCURRENCY CONTROL

Real World applications demand access to critical data. With most database Systems application becomes “contention bound” – which performance is limited not by the CPU power or by disk I/O, but user waiting on one another for data access. SQL Server employs full, unrestricted row-level locking and contention free queries to minimize and in many cases entirely eliminates contention wait times.

NO I/O BOTTLENECKS

SQL Server’s fast commit groups commit and deferred write technologies dramatically reduce disk I/O bottlenecks. While some database write whole data block to disk at commit time, SQL Server commits transactions with at most sequential log file on disk at commit time, On high throughput systems, one sequential writes typically group commit multiple transactions. Data read by the transaction remains as shared memory so that other transactions may access that data without reading it again from disk. Since fast commits write all data necessary to the recovery to the log file, modified blocks are written back to the database independently of the transaction commit, when written from memory to disk.

Decentralized in Mobile Grid Computing

2012-03-10T04:46:00.000-08:00

GRID computing systems have seen their widespread adoption lately in not only academia but also in industry, as evidenced by commercial offerings from Sun [1], HP [2], and IBM [3], among others. While most existing Grids refer to clusters of computing and storage resources which are wire-interconnected for offering utility services collaboratively, Mobile Grids (MoGs) are receiving growing attention and expected to become a critical part of a future computational Grid involving mobile hosts to facilitate user access to the Grid and to also offer computing resources [4]. A MoG can involve a number of mobile hosts (MHs), i.e., laptop computers, cell phones, PDAs, or wearable computing gear, having wireless interconnections among one another, or to access points. Indeed, a recent push by HP to equip business notebooks with integrated global broadband wireless connectivity [5], [52], has made it possible to form a truly mobile Grid (MoG) that consist of MHs providing computing utility services collaboratively, with or without connections to a wired Grid.

Current trends toward powerful multicore processors, efficient, small flash memory devices, and wireless technologies, such as IEEE 802.16 WiMAX (which are capable of delivering 10 Mbps or more over distances of miles), are seen as technological enablers of the practical MoG, while models for compensation, accounting, and regulation of these systems are being developed. For example, some authors have proposed a fair pricing strategy and optimal job allocation scheme for MoG computing including the Nash Bargaining solution to maximize Grid revenue from the user viewpoint [34]. Other researchers proposed a middleware using mobile agents for secure MoG services, addressed the heterogeneity concerns of this technology, and provided compatible interfaces to the Globus Toolkit [35]. Also underway are active research projects entertaining the use and practical applications of MoG concepts. In particular, the K*Grid project, aiming to provide a research environment for both industry and academia, has studied the use of idle resources for a great number of mobile devices and the development of a MoG platform [36]. Additionally, the AKOGRIMO project developed a blueprint for the Next Generation Grid based upon a Mobile Collaborative Business Grid model and the Mobile Dynamic Virtual Organization (MDVO) [37]. The distributed applications likely to run on these MoGs are not the traditional long-running heavy computing jobs commonly found in their wired counterparts, but instead, are lightweight, distributed mobile agents (e.g., location-aware and specifically designed, intelligent, reflective collaborative agents acting at the request of a client on the conventional Grid or within the MoG itself) whose functions will enhance both the values of the MoG and the entire global Grid [39],

[40], [41], [42]. Because sensors (e.g., GPS, compasses, accelerometers, etc.) are commonly included in many mobile devices, lightweight applications will be capable of providing local, collaborative, and interpretive processing of disparate sensor data from a number of MHs, in order to intelligently surmise macro events for MoG or global Grid clients [50], [51]. For example, “Land Warrior” [56] is a poignant use case, providing integrated battle plan management to soldiers in the field. It incorporates wearable computers, GPS, cameras, and helmet-mounted flip-down displays capable of digitally displaying maps with locations of both friendly and enemy troops. Collaborative computing is required to update maps in real-time battle scenarios. Other use cases include collaborative mobile gaming, context-aware applications for internetworked cars, and military advantages such as “Cooperative Engagement Capability” [53], [54], [55].

Due to mobility and intermittent wireless link loss, all such scenarios call for robust checkpointing and recovery to support execution, minimizing execution rewind, and recovery rollback delay penalties. Depending upon the application’s or job’s tolerance for such delay (i.e., a QoS metric), its performance can be poor or it can be rendered totally inoperative and useless. Our Reliability Driven middleware, ReD, allows an MoG scheduler to make informed decisions, selectively submitting job portions to hosts having superior checkpointing arrangements in order to ensure successful completion by 1) providing highly reliable checkpointing, increasing the probability of successful recovery, minimizing rollback delay, and 2) providing performance prediction to the scheduler, enabling the client’s specified maximum delay tolerance to be better negotiated and matched with MoG resource capabilities. Suitable for scientific applications, MoGs are particularly useful in remote areas where access to the wired Grid is infeasible, and autonomous, collaborative computing is needed [4]. MoG hosts have no guaranteed access to fixed, wired, Grid access points. Checkpointing is thus crucial for practical and feasible job completion, for without it, the MoG’s potential is severely limited.

Checkpointing saves intermediate data and machine states periodically to reliable storage during the course of job execution. Various checkpointing mechanisms have been pursued for distributed systems (whose computing hosts are wire-connected) [6], [7], [8], [9], [10], [11], [12], [13]. However, they are not suitable for the MoG because their checkpointing arrangements 1) are relatively immaterial as checkpointed data from hosts can be stored at a designated server or servers, since connections to a server are deemed reliable, of high bandwidth, and of low latency, and 2) fail to deal with link disconnections and degrees of system topological dynamicity.This paper proposes an autonomic, decentralized, QoSaware, middleware, whose function is to establish checkpointing arrangements among MHs dynamically within the MoG, allowing its constituent MHs to support practical collaborative computation. As wireless links are less reliable and MHs move at will, a given job executed by multiple MHs collaboratively, relies on efficient checkpointing to enable execution recovery upon a MoG component failure by transferring recently saved intermediate data and machine states to a substitute MoG component, so that execution can resume from the last checkpoint, saved prior to the failure (instead of starting all over again from the very beginning). Our checkpointing methodology requires no BS to achieve its function as checkpointing is handled within the MoG by keeping checkpointed data from a given MH at immediate neighboring MHs. Our methodology also facilitates encapsulation of the checkpointing function within the MoG, making it transparent to the wired-Grid or mobile client being served. Thus, in order to limit the use of relatively unreliable wireless links, and further to minimize the consumption of wireless host’s memory resources and energy, eachMHsends its checkpointed data to one selected neighboring MH, and also serves to take checkpointed data from one approved neighboring MH, realizing a decentralized form of checkpointing. Each MH in a connected MoG concurrently runs both consumer and provider algorithm functions, and is thus capable of being both a provider and a consumer of checkpointing services for some other MHs. Obviously, there are numerous ways to assign checkpointing consumers and providers among all constituent MHs within a MoG, referred to as checkpointing arrangements.

We show in this paper that the choice of checkpointing arrangement has significant bearing upon MoG system reliability and thus QoS, Quality of Service (namely, the probability that an application will complete feasibly within the bounds of the client’s a priori specified limit in terms of time frame, reliability, etc.) expected by a user’s application. Different arrangements yield differing probabilities that the checkpointed data will survive and be recoverable in the presence of host failure, or more likely and more frequently, link failure or inadvertent and intermittent disconnection of a host or hosts from the MoG.Further, it is crucial to checkpoint to a well-connected and reliable neighbor, any MH with low remaining battery capacity or poor connections to the rest of the MoG, or any MH which may be in jeopardy of being disconnected from the MoG altogether (e.g., moving out of range). An efficient arrangement thus aims to identify superior assignments of providers and consumers of checkpointing services, among all MHs within the MoG in order to ensure collaborative job execution can complete reliably with smaller probability of experiencing unrecoverable failures. This paper shows that the globally optimal checkpointing arrangement is an NPcomplete problem [32]. ReD is thus based upon a heuristic formulation that attains arrangements of superior reliability. Its efficient convergence is promoted by a derived and supportive, simple clustering algorithm, allowing concurrent operation on small clusters of hosts individually, rather than on a single large global system.

The rest of this paper is organized as follows: Section 2 outlines related checkpointing work in wire-connected Grid systems and wireless systems with BSs. Section 3 discusses our proposed Reliability Driven (ReD) middleware, its underpinnings and efficient algorithm, and describes our simulator and the test bed implementations used to evaluate its performance. Section 4 provides simulation and test bed results, comparing ReD with a QoS-Blind methodology, called Random Checkpoint Arrangement protocol, (RCA).

ABCTRACT:

This paper deals with decentralized, QoS-aware middleware for checkpointing arrangement in Mobile Grid (MoG) computing systems. Checkpointing is more crucial in MoG systems than in their conventional wired counterparts due to host mobility, dynamicity, less reliable wireless links, frequent disconnections and variations in mobile systems. We’ve determined the globally optimal checkpoint arrangement to be NP-complete and so consider Reliability Driven (ReD) middleware, employing decentralized QoS-aware heuristics, to construct superior checkpointing arrangements efficiently. With ReD, an MH (mobile host) simply sends its checkpointed data to one selected neighboring MH, and also serves as a stable point of storage for checkpointed data received from a single approved neighboring MH. ReD works to maximize the probability of checkpointed data recovery during job execution, increasing the likelihood that a distributed application, executed on the MoG, completes without sustaining an unrecoverable failure. It allows collaborative services to be offered practically and autonomously by the MoG. Simulations and actual test bed implementation show ReD’s favorable recovery probabilities with respect to Random Checkpointing Arrangement (RCA) middleware, a QoS-blind comparison protocol producing random arbitrary checkpointing arrangements

PROJECT PURPOSE

This paper deals with decentralized, QoS-aware middleware for checkpointing arrangement in Mobile Grid (MoG) computing systems. Checkpointing is more crucial in MoG systems than in their conventional wired counterparts due to host mobility, dynamicity, less reliable wireless links, frequent disconnections and variations in mobile systems. We’ve determined the globally optimal checkpoint arrangement to be NP-complete and so consider Reliability Driven (ReD) middleware, employing decentralized QoS-aware heuristics, to construct superior check pointing arrangements efficiently. With ReD, an MH (mobile host) simply sends its checkpointed data to one selected neighboring MH, and also serves as a stable point of storage for checkpointed data received from a single approved neighboring MH. ReD works to maximize the probability of checkpointed data recovery during job execution, increasing the likelihood that a distributed application, executed on the MoG, completes without sustaining an unrecoverable failure. It allows collaborative services to be offered practically and autonomously by the MoG. Simulations and actual testbed implementation show ReD’s favorable recovery probabilities with respect to Random Checkpointing Arrangement (RCA) middleware, a QoS-blind comparison protocol producing random arbitrary checkpointing arrangements.

CHAPTER 2

System Analysis

PROBLEM DEFINITION:

EXISTING SYSTEM:

Ø Check pointing is a technique for inserting fault tolerance into computing systems. It basically consists of storing a snapshot of the current application state, and later on, uses it for restarting the execution in case of failure.

Ø Checkpointing is more crucial in MoG systems than in their conventional wired counterparts due to host mobility, dynamicity, less reliable wireless links, frequent disconnections and variations in mobile systems.

Ø Grid computing is a term referring to the combination of computer resources from multiple administrative domains to reach common goal.

Ø In existing system the losses and performance are unpredictable.

PROPOSED SYSTEM:

Ø This paper deals with decentralized, QoS-aware middleware for checkpointing arrangement in Mobile Grid (MoG) computing systems.

Ø We’ve determined the globally optimal checkpoint arrangement to be NP-complete and so consider Reliability Driven (ReD) middleware, employing decentralized QoS-aware heuristics, to construct superior checkpointing arrangements efficiently.

Ø With ReD, an MH (mobile host) simply sends its checkpointed data to one selected neighboring MH, and also serves as a stable point of storage for checkpointed data received from a single approved neighboring MH.

Ø ReD works to maximize the probability of checkpointed data recovery during job execution.

Ø The data losses and time consumption were reduced by using probability distribution as well as increases the performance evaluation.

FEASIBILITY STUDY

The feasibility of the project is analyzed in this phase and business proposal is put forth with a very general plan for the project and some cost estimates. During system analysis the feasibility study of the proposed system is to be carried out. This is to ensure that the proposed system is not a burden to the company. For feasibility analysis, some understanding of the major requirements for the system is essential.

¨ ECONOMICAL FEASIBILITY

¨ TECHNICAL FEASIBILITY

¨ SOCIAL FEASIBILITY

ECONOMICAL FEASIBILITY

This study is carried out to check the economic impact that the system will have on the organization. The amount of fund that the company can pour into the research and development of the system is limited. The expenditures must be justified. Thus the developed system as well within the budget and this was achieved because most of the technologies used are freely available. Only the customized products had to be purchased.

TECHNICAL FEASIBILITY

This study is carried out to check the technical feasibility, that is, the technical requirements of the system. Any system developed must not have a high demand on the available technical resources. This will lead to high demands on the available technical resources. This will lead to high demands being placed on the client. The developed system must have a modest requirement, as only minimal or null changes are required for implementing this system.

SOCIAL FEASIBILITY

The aspect of study is to check the level of acceptance of the system by the user. This includes the process of training the user to use the system efficiently. The user must not feel threatened by the system, instead must accept it as a necessity. The level of acceptance by the users solely depends on the methods that are employed to educate the user about the system and to make him familiar with it. His level of confidence must be raised so that he is also able to make some constructive criticism, which is welcomed, as he is the final user of the system.

System Specification:

Hardware CONFIGURATION

· Hard disk : 40 GB

· RAM : 512mb

· Processor : Pentium IV

· Monitor : 17’’Color Monitor

Software CONFIGURATION

Front End : Java
Operating System : Windows XP.

MODULES

ü ReD’s Heuristic Basis

· Packet sender

· Packet queue

· Packet receiver

ü Simulations and Test bed

ü Checkpoint

ü Performance Evaluation

MODULE DESCREPTION:

ReD’s Methodology:

An executing host is considered to be in “failure,” if wireless connections to all of its neighbors are disrupted temporarily or permanently, resulting in its isolation and inability to achieve timely delivery of intermediate or final application results to other hosts. Executing MHs with poor connectivity, have greater likelihood of experiencing failure than do those with greater connectivity and are thus in greater need of checkpointing to the best, most reliably connected providers. In order to evaluate and compare the strength of progressive checkpointing arrangements, we calculate the reliability, Ri, of the whole arrangement on the MoG structure (Mi), as depicted in Fig. 1, where each of the symbol labels on the model’s reliability diagram blocks are as previously defined (see Table 1). Link signal strength decreases inversely with the square of the distance between linked hosts. Reliability mapping for the link is thus based on this assumed signal strength profile with failure rate, _i, assumed to be constant for the small time interval, _ti, typically a few milliseconds in the mobile environment.

We conclude from Fig. 1 that no unrecoverable failure occurs under the following two cases:

Case 1: No failure at participating hosts (i.e., no isolation of any host when its results are required). A given application will run to completion without restarting or rewinding. This is true whether or not checkpointing is done.

Case 2: Failure of a participating host (isolation). The application will run to completion, with recovery assistance, provided that upon a host failure, its latest saved checkpoint data exists on the provider host, which is not itself isolated and can then be accessed by the MoG during the recovery process. This is possible only if 1) the link for sending checkpoints to the provider host did not fail when being used to checkpoint, and 2) the provider host did not fail (i.e., did not itself become isolated from the rest of the MoG) during the attempt by the MoG to access saved checkpointed data upon recovery.

Our checkpoint arrangement protocol, ReD, makes use of the underpinnings in both cases, resulting in the basis model Fig. 1 depicts. As long as all hosts running the distributed application have high connectivity (i.e., low likelihood of separation from the MoG), robust safe storage or reliable transmission to safe storage is not really needed. Only poorly connected hosts (e.g., hosts on the fringes of the MoG) need robust safe storage and reliable transmission to that safe storage. Furthermore, since host failures happen mos frequently to poorly connected hosts, it makes sense to reserve the most robust checkpoint storage (providers) for these hosts, while leaving the least robust storage for the bestconnected hosts. This way puts valuable and scarce robust checkpointing resources to work where they are most needed in reducing unrecoverable failures effectively. Essentially then, ReD continually seeks to 1) match providers having the greatest connectivity to consumers having the lowest connectivity and 2) to send checkpointed data from the consumer to the provider over the most reliable link possible in the process as host connectivities and link reliabilities vary in a dynamic and mobile MoG. Based upon Fig. 1, we assume that a consumer can checkpoint to any provider where there are N choices for picking a consumer, k, and N _ 1 choices for picking a provider, l, once the initial consumer is selected. ReD thus seeks to maximize (1), as previously defined. Because of its NP-completeness, the global optimization of this model on the dynamic MoG is not deemed feasible. Even in small clusters, e.g., 10 hosts or so, we did not consider a centralized approach for optimizing Ri to be practical. In addition to optimizing Ri, two precepts considered nearly as important in the design of ReD were convergence rate and stability. It is the average series of Ri values obtained during the course of execution that ultimately determines the QoS level presented to a user application. In a mobile environment, where checkpointing arrangements are constantly forming and being torn down, both rapid convergence to superior checkpointing arrangements and stability once those arrangements are reached, are considered to be equally important as the Ri values converged upon. ReD’s heuristic method ensures that checkpointing arrangement decisions are made locally and individually at the host level, promoting rapid convergence, while a threshold mechanism is included in order to provide stability control.

Fig. 1. ReD reliability model.

Fig. 2. Protocol function.

ReD’s Algorithm Description:

Upon initiation or refresh, if some consumer, MHk, does not have a designated provider, it begins to look for one. In doing so, it examines and compares _ka _ _a products of each of its n neighbors (a ¼ 9 neighbor ID),MHa (where _a ¼ a), sorting the products in decreasing order (max to min). Next, it transmits a checkpoint request, first to the list top host (having the greatest _ka _ _a product), e.g., MHl. In essence, a checkpoint request asks the provider,MHl’s permission to send checkpointed data to it. If prospective provider, MHl has no consumer of record, it readily grants permission and sends a positive acknowledgment back toMHk, establishing a MHk ! MHl relationship. On the other hand, if a relationship, say, MHj ! MHl already exists, MHl checks to see if the requesting consumer’s pairing reliability gain is greater than that for its existing paired consumer, i.e., is ð kjj_kl _ _lÞ = kalone > ð jjj_jl _ _lÞ = jalone a true statement? If so, it breaks its relationship with MHj by sending it a break message, and then grants permission to MHk by sending it an acknowledgment. If, on the other hand, the statement proves false, MHk is sent a negative acknowledgment, and MHl maintains the relationship,MHj ! MHl, unless otherwise severed due to mobility, or weak signal. Fig. 2 depicts ReD’s protocol messages while the pseudocode used is listed in the Appendix. We call this apparent gain in reliability, achieved by the prospective pairing of some consumer, MHk, with some provider, MHa, the pairing gain of k on a, i.e., _Gka, or GðMHk ! MHaÞ, where _Gka ¼ ð kjj_ka _ _aÞ = kalone. ReD has been enhanced since our initial preliminary work [49] to include pairing gain considerations when comparing prospective relationships as opposed to just strictly comparing alternative relationship reliabilities directly. In summary, ReD, essentially greedily, attempts to globally maximize Ri, through local, decentralized, MHk ! MHa pairing decisions. To allow for mobility, tables of connectivity and link reliabilities are updated and aged via the softstate process. A consumer periodically refreshes, checks its _ka _ _a sorted product list, and determines if it might do better to find another provider, whereupon it takes action. A provider, upon loss of relationship with a consumer, for any reason, deletes its consumer pointer and admits requests from other consumers. Finally, upon receiving a break message, a paired consumer initiates the process of finding a checkpoint provider all over again. Note that ReD is designed to be IID (meaning to run on each host independently and identically). Because messages can be lost in transmission, especially over poor wireless links, tables of host connectivity, link reliabilities, and consumer and provider pointers are maintained at hosts by the soft state registration process, e.g., due to mobility or a weak signal, a host may declare that it has lost its provider (or consumer), attempting to find a new one (or ready to admit a checkpoint request).

Pseudo code

Sender code:

Receiver code:

SIMULATIONS, TESTBED, AND RESULTS:

A series of experiments were conducted in both environments with exhaustive studies being done on the simulator and subsequent studies done in the testbed placing hosts in various relative positions to each other and averaging the results.

Simulator Description:

The simulator is a custom designed, layered environment, including a Physical Layer (wireless 801.11b employing 64 byte frames) , Link State Layer (to calculate link reliability), Connectivity Layer, (using link state reliability calculations to compute host connectivity), and the Clustering Layer (to establish and maintain clusters). The simulated checkpoint arrangement protocol, either ReD or a compared Random Checkpoint Arrangement (RCA) protocol, then operates at the Checkpoint Arrangement Layer within each of the formed clusters. RCA is not QoS-aware as is ReD, and was designed to determine consumer ! provider relationships within clusters according to randomly assigned relative strengths instead of actual host connectivity values.So while having access to QoS-aware system information, i.e., link reliabilities and host connectivities, provided by the lower link-state and connectivity layers, RCA effectively chooses to ignore these when making its checkpointing arrangement decisions, rendering it QoS-blind. So, a consumer seeking checkpointing services arbitrarily chooses a provider, while a provider uses an arbitrary method to determine whether to provide services to a requesting consumer, e.g., greatest Node ID (or randomly assigned strength). Otherwise, RCA utilizes the same supporting layer functions, checkpointing arrangement mechanisms, messages, constraints, and algorithm as ReD, in order to preserve a fair comparison. It is reasonable to compare ReD to RCA since QoS-aware decentralized checkpointing arrangement in MoGs is an emerging area of research with little comparative, or competing algorithms in existence in the literature. Thus, RCA was designed to be QoS-blind, not a lower bound, permitting a reasonable comparison, so that ReD’s improvement over any generic arbitrary arrangement choice methodology could be clearly demonstrated. The simulator gathers statistics via the Measurement Module about the performance of ReD and RCA, allowing us to adjust host density, velocity, and signal range. Gathered data included checkpointing arrangement reliability, and message counts along with average numbers of consumer hosts finding providers.

Testbed Implementation:

In order to augment and further validate the simulator results, we implemented an actual testbed, of from six (6) to twelve (12) identically configured laptop computers to fairly compare ReD versus RCA in a 10 _ 10 m indoor field. Numerous (10 to 24 hour) tests were run for both ReD and RCA in identical positional configuration permutations. Each computer, via iwspy, and Orinoco Silver 802.11b PCMCIA cards, measured received 802.11b wireless signal strength, smoothed them via moving average, and then indexed them by neighbor MAC address (ID). Hosts then mapped each measurement to respective link-reliability figures, storing them in their dynamic link-reliability arrays. Mapping calculations were based upon actual field tests of signal strength host-to-host in various positions and angular orientation with respect to each other. Ethernet signal strength measurements obtained corresponded well to those found in other work utilizing the same measurements to facilitate robot locations [33]. Utilizing this data, each host calculated its own connectivity to the rest of the MoG testbed. Hosts dynamically exchanged calculated connectivities with neighbors via short UDP/IP packets, allowing connectivity tables, indexed by neighbor ID, and sorted max to min, to facilitate both cluster formation and ReD’s checkpointing arrangement decisions. Performance data for ReD versus RCA, were obtained with received signal strength being found to vary both positionally, and temporally.

Evaluation Results:

Over 1,600, 240 hour, simulations, with 32 hosts, walking randomly (0.5 m/s) in a 100-m square area, were conducted, to obtain statistically broad and valid data. Simulation utilized the BigRed computer cluster (consisting of 180 nodes), located in the Center for Advanced Computer Studies, at the University of Louisiana at Lafayette. First, the performance of ReD was compared to that of RCA in a series of simulations under the identically configured conditions. Simulation data clearly showed ReD’s significant improvement over RCA. Subsequently, we verified ReD’s performance superiority over RCA through our working testbed. Finally, we hypothesized an intuitive and practical stabilization mechanism for ReD, utilizing pairing reliability gain threshold (i.e., we require a certain minimum threshold of pairing reliability gain before

permitting established consumer ! provider relationships to be revised) could significantly improve Ri values. Simulations validate our hypothesis.

Simulation Results:

Initial simulations compared ReD to RCA to establish MoGsystem checkpointing arrangement reliabilities, Ris, (temporal averages) attained with respect to host scheduling levels, H (refer to Table 1), ranging from 1 to 15, over wireless ranges of 20, 30, and 40 m, as shown in Figs. 3, 4, and 5, respectively. Fig. 6 shows checkpointing arrangement message-count percent reductions for ReD versus RCA. Simulation results show ReD to enjoy significant improvements over RCA in the following respects:

1. the average achieved MoG checkpointing arrangement reliabilities, Ris at most H levels.

2. Ri scaling with H.

3. ReD’s percent reduction of checkpoint message transmissions, showing far better scaling with wireless range increases than RCA.

4. ReD enjoyed 12.9 percent, 19.5 percent, and 30.3 percent fewer break message transmissions than did RCA. And

5. improvements of 43 percent, 74 percent, and 106 percent in the average number of maintained

consumer ! provider relationships for wireless ranges of 20, 30, and 40 m, respectively, an

independent indicator of superior stability (not shown in graphs).

Fig. 5 shows ReD’s arrangement reliability figure at 59 percent versus RCA’s at 6 percent for a scheduling level of H ¼ 12 scheduled hosts, within a 32 host MoG system. This figure gives the probability, p, of executing a distributed job on the arrangement for an entire checkpoint interval (e.g., 2 seconds) without sustaining a single unrecoverable failure. Conversely, on average, unrecoverable failures occur during job execution with probability 1 _ p. Therefore, unrecoverable failures will on an average, be much more significant in the background under RCA than under ReD. Every unrecoverable failure imposes significant job rewind delay during application execution. These rewind delay differences are magnified by the degree to which distributed job portions are data dependent (and not embarrassingly parallel). The adverse impact of these reliability figures means that lengthy rewind delay to a proposed job’s maximum execution time will be significantly greater under RCA control. Thus, the MoG job scheduling function will be able to commit to a much earlier job completion time under ReD. If part of the client’s QoS specifies some maximum (e.g., near real time) tolerable delay, and prospective execution time under RCA exceeds that value frequently, a significant number of jobs would not be successfully completed under RCA. ReD can provide reliability versus H level to other MoG functions such as the MoG job apportionment scheduler. Note that at lower H-levels, the reliability of the arrangement subset is better since we can choose the most reliable consumer! provider pairs from the arrangement to form our execution group. So, practically, the job scheduler would utilize projected real time reliability figures to determine at what H levels, some portions of the jobs could be run to complete within the specified maximum delay tolerance (i.e., QoS). Delineations between ReD and RCA are statistically significant exceeding 95 percent confidence intervals.

Further, previous simulations of 64 and 81 host systems have shown, that as the number of hosts and host density increase, ReD’s advantage over RCA consistently scales better, with even greater superiority in both average MoG-system reliabilities, i.e., average of Ris, and reduced checkpointing arrangement message count. To understand the root causes underlying the results obtained, consider that with increases in range, the number of hosts, and host density within the MoG, greater degrees of freedom with respect toMHk ! MHa pairing choices, exist. ReD, being QoS-Aware is better able to capitalize upon this increased freedom degree, weighing connectivity metrics to increase Ris by surgically implementing relationships with the greatest pairing reliability gain instead of arbitrary ones. To further understand results, however, we need delve into the concept of stability as earlier defined, in the context of MoG checkpointing arrangements. In the presence of mobility, the associated relocation of hosts, and changes in their resultant link reliabilities and connectivities, the established MoG checkpointing arrangement must be dynamically and continually revised in order to flexibly track optimal system reliability. Too stable (i.e., rigid) and we can’t track mobility in order to seek optimal stability, not stable enough and we risk a large degree of arrangement reshuffling, which also reduces reliability (i.e.,

reliability tracking versus stability tradeoff). ReD, in making a greater percentage of correct MHk ! MHa pairing choices than RCA, ensures less need to break (and thus revise) existing relationships in order to form new ones.

This important characteristic is demonstrated by ReD’s overall reduction in the number of break messages (Fig. 6). In the process of continually revising arrangements some consumers will inevitably be unpaired (in any system snapshot), and in the process of finding a provider to pair with. Upon obtaining a provider, these unpaired consumers sometimes cause that provider to break with its existing consumer, causing that consumer to likewise seek a provider, etc., with a resulting cascading reshuffling of the system checkpointing arrangement. Reshuffling has a negative affect on average system reliability due to the increased message traffic and a certain background percentage of unpaired consumers. ReD, thus outperforms RCA due to both increased Ris obtained and improved system stability. In summary, because RCA, is not QoSaware, it tends to produce arbitrary checkpointing arrangements due to arbitrary MHk ! MHa pairing choices with greater likelihood of connectivity loss under mobility, causing instability and suboptimal checkpointing arrangements.

The superior performance shown by ReD can be attributed to both its resulting superior checkpointing arrangements and its superior stability characteristics. Due to its superior stability characteristics, ReD requires significantly fewer checkpointing arrangement maintenance messages as depicted in Fig. 6, with a decrease of more than 50 percent in the number of consumer initiated checkpoint repeat requests at 40 m effective wireless range. As expected, this advantage also scales better with increased range and has been shown previously to scale better with increased numbers of hosts and host density in simulations with up to 81 hosts.

Testbed Results:

Results obtained from testbed implementation corroborate our simulation studies. Fig. 7 depicts the results showing ReD’s superior system reliability as compared to RCA. Because, simulations were able to utilize a large number of hosts, and full mobility, a situation not afforded to the testbed implementation, ReD’s superior performance results were more salient in the simulator than those depicted in Fig. 7. However, we nevertheless see improvement by approximately 200 percent corresponding with a scheduling level, H, of 10 MHs in a 12-host system. The testbed was run indoors, for numerous 24 hours periods, under a number of static positional permutations with results averaged to obtain the Fig. 7 results. Signal strength was found to vary both temporally and positionally. Analogous to the results obtained in simulations, and in addition to its superior system reliability achievement, ReD requires 44.4 percent fewer checkpointing arrangement and maintenance messages on average than did RCA in the testbed implementation. As an indicator of superior stability, ReD enjoyed a break message decrease of more than 90 percent as depicted in Fig. 8.

Fig. 7. Comparison of ReD versus RCA testbed system reliabilities.

Fig. 8. ReD versus RCA in testbed message percent change.

DECENTRALIZED CHECKPOINTING IN THE MOG:

This work focuses on the MH checkpointing arrangement mechanism, seeking superior checkpointing arrangements to maximize the probability of distributed application completion without sustaining an unrecoverable failure. It deals with MoG checkpointing among neighboring MHs without any access point or BS. Our main focus in checkpointing arrangement lies in MH connectivity, with host failure being the failure of all of the host’s connecting wireless links. As the MH battery exhibits a much larger MTBF, than the wireless links, its failure impact will be considered later. Let MHk ! MHl define a checkpointing relationship between MHk and MHl, functioning as the consumer and provider of checkpointing services, respectively. The set of all such directed relationships, on a MoG instance, is called a checkpointing arrangement. We define stability, in the context of a checkpointing arrangement, as one where no consumer or provider prefers another provider or consumer, respectively, to its current partner in the relationship, and all consumers and providers have found providers and consumers, respectively. Such an arrangement is called stable. Distinct arrangements result in differing MoG system reliability values and potential instabilities due to positional and wireless signal strength variations among MHs. Less reliable wireless links are more prone to frequent and intermittent disconnections. With MHs, relative locations, velocities, intervening obstacles, multipathing, interference, and other effects all partially determine link strength and reliability [38]. Our Reliability Driven methodology, is made QoS-aware, via wireless measurements, of the reliabilities of links between MHs in the MoG. It aims to enhance the service quality (QoS) received by a distributed application while executing on the MoG. It makes use of link reliability values to dynamically maintain superior checkpointing arrangements in the mobile environment.

Theoretical Considerations:

Laptops, wearable computers, PDAs, cell phones, and other MHs may exhibit high mobility. Practically, it is desirable for our MoG to be flexible, ad-hoc, efficient, adaptive, and robust. In a mobile environment, intermittent signal degradation, interferences, and outright signal loss are major sources of both temporary and long term disconnection of certain MHs from the MoG. Battery powered and energy-limited, with low processing and memory capacity, MHs highly desire conservation of energy, CPU cycles, and memory. In this environment, any algorithm used for checkpointing arrangement must quickly converge to a superior solution.

As no nearby BS is guaranteed, the MoG of interest calls for a decentralized checkpointing methodology, storing checkpoints at neighboring MHs. Since the QoS delivered by the MoG is sensitive to the checkpointing methodology employed, it is essential to decide which MHs send checkpointed data to what other MHs for safe storage (checkpointing arrangement). When establishing the arrangement, it is intuitive for MHs with stronger wireless connections to other MHs to serve as the more desirable points of stable storage for checkpointed data. Also, those MHs, which are weakly connected, and possibly moving out of range (imminent failure), urgently need access to strongly connected peers to quickly store checkpointed data, preventing its irrevocable loss. MH battery failure can be considered similarly. To automatically and dynamically determine which checkpointing arrangements are more reliable and stable at a given snapshot time, t, each MH is assumed to be aware of the quality of its own wireless connections as well as the wireless connections of its neighbors (i.e., QoS-aware). Considering the aforementioned MoG operational requirements, we list below, the formulation upon which our desirable, QoS-aware, decentralized, checkpointing arrangement methodology is derived. Problem formulation. Let Ck represent the consumer function of Host k, transmitting checkpointed data to be handled by the provider function, Pl, of Host l. Further, let Sj represent the recovery execution function at Host j, scheduled to resume execution of the checkpointed process affected by the potential failure of Host k. To ensure efficient handoff and resumption of the process on Sj, it is reasonable to assume that j will be a neighbor of l. Let Mi define an instance of a connected MoG at time ti, composed of N _ 2 MHs, under the worst case where all MHs are desired to collaborate via some wireless paths in order to execute a client’s distributed application. In essence,Mi is a connected graph with MHs as vertices and wireless links as edges. Let _tcp and _tps be the minimum transmission times required to communicate checkpointed data from Ck to Pl prior to failure at k, and to recover checkpointed data from Pl to Sj after the failure at k, respectively. In both instances, hosts communicating the checkpointed data are neighbors, giving rise to _tcp ¼ _tps.

Thus, we define a short time interval, _ti ¼ _tcp ¼ _tps, centered on ti, during which any wireless link must operate failure-free in order to communicate checkpoint data with the minimum delay. We designate _i as the mapped, link failure rate (for a given link) assessed at ti, and reasonably assumed to be constant over _ti. Next, we define the term, “connectivity,” k, of a host, MHk, (functioning as consumer or provider) to be the parallel reliability of wireless links to all neighbors in Mi, i.e., k ¼ ð_k1;jj_k2;jj_kl;jj _ _ _ jj_kn;Þ, k 6¼ l, where _kl is the link reliability over interval _ti from MHk to some neighbor MHl given as _kl ¼ e__i_ti . Thus, k > g

implies that MHk is better connected on Mi than is MHg. Connectivity expresses the probability that the referenced host will never entirely lose all its wireless links to the MoG for the entire duration, _ti. Given a worst case, well connected, MoG instance, Mi, at time snapshot ti, consisting of n MHs, the number, _, of complete checkpoint arrangements grows factorially in N. ReD maximizes where _k is the connectivity of MHk functioning as the consumer, _l is the connectivity of MHl functioning as the provider, _kl is the reliability of the wireless link connecting hosts k and l in Mi, N is the total number of hosts in Mi and Ri is the arrangement reliability on the MoG instance. With feasible hardware support, _k, _l, and _kl can reasonably be assumed to be independent and generally uncorrelated. In essence, Ri is the probability of the entire MoG operating successfully over time interval, _ti. By maximizing Ri to achieve Ri max , we maximize the probability that either no consumer fails (i.e., becomes disconnected from the MoG) during _ti, or if it does fail, the MoG will always succeed in access to the needed most current checkpoint, by one or more links, because the provider holding the checkpoint remains connected to the MoG. On Mi, let _i be a directed graph, _i ¼ G ¼ ðV ;EÞ, representing a unique checkpointing arrangement. It is a snapshot of all consumer ! provider relationships on Mi at time ti. EachMHk is chosen from the set of vertices, as Vk and each wireless link, _kl, is chosen from the set of edges, i.e., Ekl. Given _i, our problem initially appears to be that of finding the optimum checkpoint arrangement, _i max, on Mi, yielding the greatest reliability, Ri max . This problem, _i max , seeks some checkpoint arrangement or arrangements, all yielding a reliability value, Ri max , i.e., an optimal singularity.

However, such a solution cannot be practically realized due to intrinsic measurement uncertainties in hardware, software, and the environment. In utilizing real MH network devices to measure link strengths so as to establish and propagate host connectivities in the MoG, we must allow for measurement uncertainties. We utilize __ to allow for temporal differences between the sampling time, t1, and arrangement decision time, t2, and it is important since data sampled at one host is often acted upon a little later by some other host in building checkpoint arrangements. Any device signal measurement inaccuracies, in the wireless environment, are accounted for by _ . Finally, we consider _, since special position and orientation contribute to measurement differences among hosts. The total, ever present, uncertainty, _T , from all sources is the statistical combination and _ (see explanation). Hence, while there are likely to be additional sources of uncertainty (e.g., interference and measurement, reliability assessment mapping, and temporal differences), the salient point here is that we must allow for this uncertainty in our calculations. Allowance for _T requires that we revise our definition of “maximum” or “optimal.” If we assume some small average _T at each MH, the uncertainty introduced into the calculation of the “optimal” arrangement will depend statistically upon _T and the number of MHs in the MoG instance. Thus, some uncertainty is introduced into any possible determination of the “optimal” arrangement.

Therefore, under such conditions, no optimal singularity can ever be determined. Instead, the best we can hope for is that we can determine whether or not, under any instance, Mi, there exists a checkpoint arrangement, _i, whose calculated reliability, Ri, is within some desired range of the best possible pairing optimal, _i max , assuming all measurements were precise. In other words we want to test whether Rth _ Ri _ Ri max is a true statement (where Rth is the general lower bound threshold selected to allow for average uncertainty and to require arrangement stability, and Ri max is the theoretical upper bound maximum). A stable arrangement is defined as one where no consumer in _i, can improve arrangement reliability, from its perspective, by successfully pairing with a better-connected provider in Mi, or equally, one where no provider in _i can improve arrangement reliability from its perspective, by successfully pairing with a more needy consumer in Mi. An unstable arrangement is likely to reorganize in a short order in favor Authorized licensed use limited to: Sreenivisa Inst of Tech and Management Studies. The practical introduction of naturally occurring uncertainty and the requirement for stability simplifies our problem to that of a decision problem, where the decision is: “have we found a stable checkpointing arrangement, _i whose calculated value holds the relationship, Ri _ Rth (yes/no)? This problem is called _ithresh. In seeking to find a stable solution to the problem _ithresh, we note that it bears some similarities to the wellknown NP-complete decision problem: “Stable Marriage Problem with Incomplete Lists and Ties, i.e., SMPILT [43].” We show that SMPILT can be transformed in a polynomial way into _ithresh. Theorem. _ithresh is NP-Complete.

Requirements Analysis

This document intends to set out the Customer requirements that were agreed at the Customer meeting, in a formal manner. These requirements have then been further developed by identifying actors, creating use-cases and identifying how the actors will interact with the system. Initial class diagrams have also been designed, including the identification of class responsibilities and collaborations between classes. This document only concerns itself with very high-level abstract analysis; however, it will be extended and adapted, by the Design team, into the high-level and eventually low-level design. In general, requirements are partitioned into functional requirements and non-functional requirements. Functional requirements are associated with specific functions, tasks or behaviours the system must support, while non-functional requirements are constraints on various attributes of these functions or tasks. In terms of the ISO quality characteristics for evaluation, the functional requirements address the quality characteristic of functionality while the other quality characteristics are concerned with various kinds of non-functional requirements. Because non-functional requirements tend to be stated in terms of constraints on the results of tasks which are given as functional requirements (e.g., constraints on the speed or efficiency of a given task), a task-based functional requirements statement is a useful skeleton upon which to construct a complete requirements statement. That is the approach taken in this work. It can be helpful to think of non-functional requirements as adverbially related to tasks or functional requirements: how fast, how efficiently, how safely, etc., is a particular task carried out by a particular system?

FUNCTIONAL REQUIREMENTS:

Provide an overview of the system and some additional information to place the system in context. A software requirements specification (SRS) is a comprehensive description of the intended purpose and environment for software under development. The SRS fully describes what the software will do and how it will be expected to perform.

An SRS minimizes the time and effort required by developers to achieve desired goals and also minimizes the development cost. A good SRS defines how an application will interact with system hardware, other programs and human users in a wide variety of real-world situations. Parameters such as operating speed, response time, availability, portability, maintainability, footprint, security and speed of recovery from adverse events are evaluated.

USER REQUIREMENTS

Provide requirements of the system, user or business, taking into account all major classes/categories of users. Provide the type of security or other distinguishing characteristics of each set of users. List the functional requirements that compose each user requirement. As the functional requirements are decomposed, the highest level functional requirements are traced to the user requirements. Inclusion of lower level functional requirements is not mandatory in the traceability to user requirements if the parent requirements are already traced to them.

User requirement information can be in text or process flow format for each major user class that shows what inputs will initiate the system functions, system interactions, and what outputs are expected to be generated by the system. The scenarios should be comprehensive, to the extent that all user types and all major functions are covered. Give each user requirement a unique number. Typically, user requirements have a numbering system that is separate from the functional requirements. Requirements may be labeled with a leading “U” or other label indicating user requirements.

NON FUNCTIONAL REQUIREMENTS:

3.3.1 Usability

Resist making statements such as “The system shall be user friendly”. It is entirely insufficient. The non-functional requirements document template breaks the section down into seven subsections and prompts for completion of each section. Be specific about the mechanism by which each aspect will be met. For example, in the section ‘Learnability’ say whether the user is expected to learn how to use the system by looking up the help or using a tutorial or whether the system can be put into a ‘learner mode’ whereby it guides a learner user through a use case by offering detailed prompts at every step. If there is to be no documentation at all and the system is to be learnt from the developers demonstrating it to the users, then say so.

3.3.2 Reliability

Reliability is notoriously difficult to test before the system goes live as it relies on continuous use and metrics. Some guidance here on what is realistic to expect should be sought in consultation with stakeholders. Figures should be defined on the basis of the metrics available for existing systems and the expectations for improvement or relaxation of these values. It will only be known if the system meets these requirements if proper metrics and requirements gathering are put in place. Notes to this effect should be included. If no serious attempt is to be made to do this, then write ‘None’ in each section.

3.3.3 Performance

Performance can and should be tested early on the development as part of the architectural development phase. Care needs to be taken to specify throughput and response times in terms of the creation and processing of major data entities with reference to specific use cases in the use case model. Ensure that the requirements are written in such a way that the testing of them will be straightforward. If the system is to share infrastructure resources with other systems specify what proportion of those resources must be available if the response times are to be met. Also include any dependencies on outside systems and specify how these are expected to respond if resource and throughput targets are to be met.

3.3.4 Security

Security includes all steps that are to be taken to secure the system against both voluntary and involuntary corruption. This includes management of usernames and passwords; encryption of data transfers both internally and across external systems such as the internet; firewalls and protection against viruses, Trojans, worms and all kinds of malicious code attacks including denial of service. It may be useful to refer to the standards specified in section 8.3 under implementation constraints.

3.3.5 Supportability

Specify ease of installation, configuration and testing in terms both of the time to achieve the goal and specific means for achieving it. Consider, for example, installation software and scripts, use cases for configuration and automatic self-testing. Think carefully about how each of these requirements will actually be tested.

3.3.6 Infrastructure Requirements

These should include description of existing or new hardware, software and networks on which the system is expected to run. Create a deployment diagram, or equivalent which shows all processing nodes, peripherals and communication links. Specify the required capacity for each of these, or, where the system runs on existing infrastructure, the amount of capacity that needs to be available for the system to meet its performance requirements. Also specify any external systems of services upon which this system will depend in terms of the performance that is required of these systems if this system is to meet its performance targets.

3.3.7 Implementation Constraints

A constraint is a requirement which leaves no design option. Implementation constraints, rather than describing what the system will do, describe constraints on the design by which what it is to do will be achieved. If there is no constraint in a section, e.g. the developers could use any language they like then say so. Otherwise describe just the constraint.

When referring to system interfaces, legacy systems and databases refer to the design documentation for these. Add important diagrams to Appendix A and refer to them in the text. If there is insufficient information about these external systems then mention that this information will need to be completed for the purposes of the development of this system.

LITRATURE SURVEY:

From Cluster Grids to- ward Mobile Collaborative Business Grids Based on the definition of Grid Types in the term Grid can be seen as a summary term for at least the following different flavours of “Grids”.

Distributed Enterprise Grids or Intra Grids:

As current security models available in commercially supported Grid toolkits or even recent developments in research do not meet the ex pectations of commercial environ- ments for resource sharing across or ganisational boundaries many deploy- ments of Grids as of today are operating behind companies firewalls. These Intra Grids may however be already geographically dispersed and are typically connected with standard network equipment.

1.2.2 Utility Grid Services:

This type of Grids can be seen as the natural next step in the evolution and is similar to Intra Grids but is additionally able to be extended on demand e.g. with computational resources offered by a third party as needed. This definition assumes that resource providers sell their resources e.g. for computation as a utility.

1.2.3 Managed Hosted Grids:

In extension to the rather limited view in [7] defining this Grid Type as an Intra Grid operating only within a single company we think this type of third party Managed Grids will emerge as well for the case where a resource owner wants to offer its resources as an utility or even as participant in collaborative business Grids.

1.2.4 An adaptive checkpointing protocol:

Numerous mathematical approaches have been proposed to determine the optimal checkpoint interval for minimizing total execution time of an application in the presence of failures. These solutions are often not applicable due to the lack of accurate data on the probability distribution of failures. Most current checkpoint libraries require application users to define a fixed time interval for checkpointing. The checkpoint interval usually implies the approximate maximum recovery time for single process applications. However, actual recovery time can be much smaller when message logging is used. Due to this faster recovery, checkpointing may be more frequent than needed and thus unnecessary execution overhead is introduced. In this paper, an adaptive checkpointing protocol is developed to accurately enforce the user-defined recovery time and to reduce excessive checkpoints. An adaptive protocol has been implemented and evaluated using a receiver-based message logging algorithm on wired and wireless mobile networks. The results show that the protocol precisely maintains the user-defined maximum recovery times for several traces with varying message exchange rates. The mechanism incurs lour overhead, avoids unnecessary checkpointing, and reduces failure free execution time.

1.2.5 Recoverable mobile environment: design and trade-off analysis:

The mobile wireless environment poses challenging problems in designing fault-tolerant systems because of the dynamics of mobility, and limited bandwidth available on wireless links. Traditional fault-tolerance schemes, therefore, cannot be directly applied to these systems. Mobile systems are often subject to environmental conditions which can cause loss of communications or data. Because of the consumer orientation of most mobilesystems, run-time faults must be corrected with minimal (if any) intervention from the user. The fault-tolerance capability must, therefore, be transparent to the user. The paper presents recovery schemes for the failure of amobile host. It portrays the limitations of the mobile wireless environment, and their impact on recovery protocols. The adaptation of well-known recovery schemes is presented which suit the mobile environment. The performance of these schemes has been analyzed to determine those environments where a particular recovery scheme is best suited. The performance of the recovery schemes primarily depends on: the wireless bandwidth; the communication-mobility ratio of the user; and the failure rate of the mobile host.

CHAPTER 3

System Design:

The purpose of the design phase is to plan a solution of the problem specified by the requirement document. This phase is the first step in moving from problem domain to the solution domain. The design of a system is perhaps the most critical factor affecting the quality of the software, and has a major impact on the later phases, particularly testing and maintenance. The output of this phase is the design document. This document is similar to a blue print or plan for the solution, and is used later during implementation, testing and maintenance.

The design activity is often divided into two separate phase-system design and detailed design. System design, which is sometimes also called top-level design, aims to identify the modules that should be in the system, the specifications of these modules, and how they interact with each other to produce the desired results. At the end of system design all the major data structures, file formats, output formats, as well as the major modules in the system and their specifications are decided.

During detailed design the internal logic of each of the modules specified in system design is decided. During this phase further details of the data structures and algorithmic design of each of the modules is specified.

The logic of a module is usually specified in a high-level design description language, which is independent of the target language in which the software will eventually be implemented. In system design the focus is on identifying the modules, whereas during detailed design the focus is on designing the logic for each of the modules. In other words, in system design the attention is on what components are needed, while in detailed design how the components can be implemented in software is the issue.

During the design phase, often two separate documents are produced. One for the system design and one for the detailed design. Together, these documents completely specify the design of the system. That is they specify the different modules in the system and internal logic of each of the modules.

A design methodology is a systematic approach to creating a design by application of set of techniques and guidelines. Most methodologies focus on system design. The two basic principles used in any design methodology are problem partitioning and abstraction. A large system cannot be handled as a whole, and so for design it is partitioned into smaller systems. Abstraction is a concept related to problem partitioning. When partitioning is used during design, the design activity focuses on one part of the system at a time. Since the part being designed interacts with other parts of the system, a clear understanding of the interaction is essential for properly designing the part. For this, abstraction is used. An abstraction of a system or a part defines the overall behavior of the system at an abstract level without giving the internal details.

While working with the part of a system, a designer needs to understand only the abstractions of the other parts with which the part being designed interacts. The use of abstraction allows the designer to practice the "divide and conquer" technique effectively by focusing one part at a time, without worrying about the details of other parts.

Like every other phase, the design phase ends with verification of the design. If the design is not specified in some executable language, the verification has to be done by evaluating the design documents. One way of doing this is thorough reviews. Typically, at least two design reviews are held-one for the system design and one for the detailed and one for the detailed design.

Software Development Life Cycle

The trends of increasing technical complexity of the systems, coupled with the need for repeatable and predictable process methodologies, have driven System Developers to establish system development models or software development life cycle models.

Nearly three decades ago the operations in an organization used to be limited and so it was possible to maintain them using manual procedures. But with the growing operations of organizations, the need to automate the various activities increased, since for manual procedures it was becoming very difficult, slow and complicated. Like maintaining records for a thousand plus employees company on papers is definitely a cumbersome job. So, at that time more and more companies started going for automation.

Since there were a lot of organizations, which were opting for automation, it was felt that some standard and structural procedure or methodology be introduced in the industry so that the transition from manual to automated system became easy. The concept of system life cycle came into existence then. Life cycle model emphasized on the need to follow some structured approach towards building new or improved system. There were many models suggested. A waterfall model was among the very first models that came into existence. Later on many other models like prototype, rapid application development model, etc were also introduced.

System development begins with the recognition of user needs. Then there is a preliminary investigation stage. It includes evaluation of present system, information gathering, feasibility study, and request approval. Feasibility study includes technical, economic, legal and operational feasibility. In economic feasibility cost-benefit analysis is done. After that, there are detailed design, implementation, testing and maintenance stages.

In this session, we'll be learning about various stages that make system's life cycle. In addition, different life cycles models will be discussed. These include Waterfall model, Prototype model, Object-Oriented Model, spiral model and Dynamic Systems Development Method (DSDM).

SPIRAL MODEL

SPIRAL MODEL was defined by Barry Boehm in his 1988 article, “A spiral Model of Software Development and Enhancement. This model was not the first model to discuss iterative development, but it was the first model to explain why the iteration models.

The steps for Spiral Model can be generalized as follows:

· A preliminary design is created for the new system.

· A first prototype of the new system is constructed from the preliminary design. This is usually a scaled-down system, and represents an approximation of the characteristics of the final product.

· A second prototype is evolved by a fourfold procedure:

Ø Evaluating the first prototype in terms of its strengths, weakness, and risks.

Ø Defining the requirements of the second prototype.

Ø Planning an designing the second prototype.

Ø Constructing and testing the second prototype.

The existing prototype is evaluated in the same manner as was the previous prototype, and if necessary, another prototype is developed from it according to the fourfold procedure outlined above.

The preceding steps are iterated until the customer is satisfied that the refined prototype represents the final product desired.

The final system is constructed, based on the refined prototype.

The final system is thoroughly evaluated and tested. Routine maintenance is carried on a continuing basis to prevent large scale failures and to minimize down time.

The following diagram shows how a spiral model acts like:

ADVANTAGES:

· Estimates(i.e. budget, schedule etc .) become more relistic as work progresses, because important issues discoved earlier.

· It is more able to cope with the changes that are software development generally entails.

You are now ready to edit, run, and compile the project with DJava..

Spiral Model Description

The development spiral consists of four quadrants as shown in the figure above

Quadrant 1: Determine objectives, alternatives, and constraints.

Quadrant 2: Evaluate alternatives, identify, resolve risks.

Quadrant 3: Develop, verify, next-level product.

Quadrant 4: Plan next phases.

Although the spiral, as depicted, is oriented toward software development, the concept is equally applicable to systems, hardware, and training, for example. To better understand the scope of each spiral development quadrant, let’s briefly address each one.

Quadrant 1: Determine Objectives, Alternatives, and Constraints

Activities performed in this quadrant include:

1. Establish an understanding of the system or product objectives—namely performance, functionality, and ability to accommodate change.

2. Investigate implementation alternatives—namely design, reuse, procure, and procure/ modify

3. Investigate constraints imposed on the alternatives—namely technology, cost, schedule, support, and risk. Once the system or product’s objectives, alternatives, and constraints are understood, Quadrant 2 (Evaluate alternatives, identify, and resolve risks) is performed.

Quadrant 2: Evaluate Alternatives, Identify, Resolve Risks

Engineering activities performed in this quadrant select an alternative approach that best satisfies technical, technology, cost, schedule, support, and risk constraints. The focus here is on risk mitigation. Each alternative is investigated and prototyped to reduce the risk associated with the development decisions. Boehm describes these activities as follows:

. . . This may involve prototyping, simulation, benchmarking, reference checking, administering user
questionnaires, analytic modeling, or combinations of these and other risk resolution techniques.

The outcome of the evaluation determines the next course of action. If critical operational and/or technical issues (COIs/CTIs) such as performance and interoperability (i.e., external and internal) risks remain, more detailed prototyping may need to be added before progressing to the next quadrant. Dr. Boehm notes that if the alternative chosen is “operationally useful and robust enough to serve as a low-risk base for future product evolution, the subsequent risk-driven steps would be the evolving series of evolutionary prototypes going toward the right (hand side of the graphic) . . . the option of writing specifications would be addressed but not exercised.” This brings us to Quadrant 3.

Quadrant 3: Develop, Verify, Next-Level Product

If a determination is made that the previous prototyping efforts have resolved the COIs/CTIs, activities to develop, verify, next-level product are performed. As a result, the basic “waterfall” approach may be employed—meaning concept of operations, design, development, integration, and test of the next system or product iteration. If appropriate, incremental development approaches may also be applicable.

Quadrant 4: Plan Next Phases

The spiral development model has one characteristic that is common to all models—the need for advanced technical planning and multidisciplinary reviews at critical staging or control points. Each cycle of the model

culminates with a technical review that assesses the status, progress, maturity, merits, risk, of development efforts to date; resolves critical operational and/or technical issues (COIs/CTIs); and reviews plans and identifies COIs/CTIs to be resolved for the next iteration of the spiral.

Subsequent implementations of the spiral may involve lower level spirals that follow the same quadrant paths and decision considerations

ARCHITECTURAL DIAGRAM:

System Architecture:

DATAFLOW DIAGRAM:

Decompose the context level diagrams to determine the functional requirements. Data flow diagrams should be decomposed down to the functional primitive level. These diagrams are further decomposed during design.

OBJECT ORIENTED ANALYSIS AND DESIGN:

A large number of libraries with ready-made objects for UML diagrams and task-oriented templates let you create UML diagrams of any complexity without effort.

Activity diagram
Use case diagram
Sequence diagram
Collaboration diagram
State chart diagram
Class diagram
Component diagram
Deployment diagram

4.2 Use-Cases:

We have identified 2 actors in these diagrams, the actual Machine Users and the Unix Developers. The Machine user can; begin using the system – this represents whichever method the user will use in order to make initial interaction with the system. For example, they may need to turn the system on via a button, simply turn the key in the ignition or some over method. They can also view a page, click on a link or back button, scroll up and down and close the system.

The Microsoft Developer inherits all these use cases, as well as being able to upload an html file and view a list of problems.

Use Case Diagram:

Class Diagram:

We have identified 5 classes in total. A Lexer class and a Parser class - which comprise the Analyser package – a ParsedTreeStructure class, a Renderer class and a Frontend class. The Lexer’s job is to build a set of tokens from a source file. The Parser uses these tokens built and deciphers their types. It then builds the tokens seen into nodes and parses them to the ParsedTreeStructure class, where a tree structure of nodes is stored. This tree is then used by the Renderer class to form a model of the page, which is in turn, is used by the Frontend in order to display the final rendered page.

Activity Diagrams:

These activity diagrams show how the use-cases interact with the system and interface. The User starts by initially interacting with the system. The main page is then rendered by the system and it is displayed by the interface, which the user can view. From here the user can click on a link, scroll or close the system. If they chose to click a link, the system renders the new page and it is displayed by the interface, which brings the user back to viewing. If the user chooses to scroll, the system will readjust the page and the interface will display the new snapshot of the page, which also brings the user back to viewing. If the user chooses to close the system, the activity diagram finishes in an exit state.

Sequence Diagram:

A sequence diagram shows, as parallel vertical lines (lifelines), different processes or objects that live simultaneously, and, as horizontal arrows, the messages exchanged between them, in the order in which they occur. This allows the specification of simple runtime scenarios in a graphical manner. For instance, the UML 1.x diagram on the right describes the sequences of messages of a (simple) restaurant system. This diagram represents a Patron ordering food and wine, drinking wine then eating the food, and finally paying for the food. The dotted lines extending downwards indicate the timeline. Time flows from top to bottom. The arrows represent messages (stimuli) from an actor or object to other objects. For example, the Patron sends message 'pay' to the Cashier. Half arrows indicate asynchronous method calls. The UML 2.0 Sequence Diagram supports similar notation to the UML 1.x Sequence Diagram with added support for modeling variations to the standard flow of events.

Collaboration Diagram:

This diagram shows the collaboration between the 5 classes and demonstrates the order of events that will take place when a page is loaded.

4: displayPage()

:Frontend

The Lexer will build tokens based on the text it has been given, the Parser then deciphers types of tokens that are generated by the Lexer class. These are then parsed to the Tree Structure in the form of nodes, which are then assembled into a tree structure. Next the Renderer class uses these nodes to build the page and finally the Frontend class takes what the Renderer has created and displays it for the user.

State Chart Diagram:

State diagrams are used to give an abstract description of the behavior of a system. This behavior is analyzed and represented in series of events, that could occur in one or more possible states. Hereby "each diagram usually represents objects of a single class and track the different states of its objects through the system.

Component Diagram:

Components are wired together by using an assembly connector to connect the required interface of one component with the provided interface of another component. This illustrates the service consumer - service provider relationship between the two components.

An assembly connector is a "connector between two components that defines that one component provides the services that another component requires. An assembly connector is a connector that is defined from a required interface or port to a provided interface or port.

Repository Management process

2012-03-10T04:42:00.000-08:00

1.1 INTRODUCTION OF PROJECT

ORMS is a Web based application that enables users to work in a multi-user platform. Architecture of ORMS is based on client-server pattern. End users use ORMS from client end. Various requests are sent to server from clients. ORMS server validates the identity of a client, processes the client request, and then sends the answer back to the client. A central repository is maintained on the server. There can be multiple repositories inside the central repository. The central repository is nothing but a workspace in the ORMS server, where various repositories are stored.

ORMS database can be implemented using, Oracle or SQL Server. An end user can utilize ORMS functionalities, such as checking in, or checking out a file from an application, such as episodic Editor, or Process Editor. However you can use ORMS commands only for the concerned type of document from an editor. For example, you can issue an ORMS command for a context map from IDT Developer, and a presentation file from Presentation Editor.

ORMS is a central repository for all content (all Simulations, Documentation, Workflows, Questions, Banks, Customized templates etc.) created using as well as any third party content. At any point of time, users having the Developer or the Remote Capture license on their desktop can collaborate, rapidly upload/ download files, have check in/check out ability, have version control and management of content. The Administrator can give read/write/edit/access rights to users by process/role/workgroup. It is an essential component in extending the usage and reach of the Remote Capture licenses throughout the Enterprise to enable collaborative authoring among groups.

PURPOSE OF PROJECT

OBJECTIVES:

The main objective of this project is to design a website for repository to simplify search for the user and make a user friendly environment with a high security level.

SCOPE:

This application can be used by any administrator to maintain the repository management, daily downloads, user registration etc..that has been stored in the database.

EXISTING SYSTEM:

Less interactive.

Semi automated.

No web tool technology is used

No powerful security.

No required levels of repositories.

Burden on central repository.

PROPOSED SYSTEM:

Providing fast accessibility to the users.

Introducing repository managers.

Improved security.

More feasibility.

Using web tools.

NEED FOR COMPUTERIZATION:

We all know the importance of computerization. The world is moving ahead atlightning speed and everyone is running short of time. One always wants to get the informationand perform a task he/she/they desire(s) within a short period of time and too with amount ofefficiency and accuracy. The application areas for the computerization have been selected onthe basis of following factors:

Ø Minimizing the manual records kept at different locations.

Ø There will be more data integrity.

Ø Facilitating desired information display, very quickly, by retrieving information from users.

Ø Facilitating various statistical information which helps in decision-making?

Ø To reduce manual efforts in activities that involved repetitive work

Ø Updating and deletion of such a huge amount of data will become easier.

1.2 SYSTEM ANALYSIS

1.2 ANALYSIS MODEL

SDLC METHDOLOGIES

SPIRAL MODEL

The steps for Spiral Model can be generalized as follows:

· A preliminary design is created for the new system.

· A first prototype of the new system is constructed from the preliminary design. This is usually a scaled-down system, and represents an approximation of the characteristics of the final product.

· A second prototype is evolved by a fourfold procedure:

1. Evaluating the first prototype in terms of its strengths, weakness, and risks.

2. Defining the requirements of the second prototype.

3. Planning a designing the second prototype.

4. Constructing and testing the second prototype.

· The existing prototype is evaluated in the same manner as was the previous prototype, and if necessary, another prototype is developed from it according to the fourfold procedure outlined above.

· The preceding steps are iterated until the customer is satisfied that the refined prototype represents the final product desired.

· The final system is constructed, based on the refined prototype.

· The final system is thoroughly evaluated and tested.Routine maintenance is carried on a continuing basis to prevent large scale failures and to minimize down time.

The following diagram shows how a spiral model acts like:

Fig 2.1 Spiral Model

STUDY OF THE SYSTEM

In the flexibility of the uses the interface has been developed a graphics concept in mind, associated through a browser interface. The GUI’S at the top level have been categorized as

Administrative user interface

1.The operational or generic user interface

MODULES:

The project has three main modules:

1. Administrator

2. Repository Manager

3. User

ADMINISTRATOR:

· Create, edit or delete Users

· Create, edit or delete Groups

· Create, edit or delete Repositories

· Add users to Groups

· Remove Users to Groups

· After the repository is created, administrator assigns a user as a repository manager.

· Repository manager performs all other internal managerial tasks for maintaining a repository.

REPOSITORY MANAGER:

View the current status of the files in the Repository
Update Users to a Group
Edit the email template applicable to a repository
Define and edit Access Rights
Create and edit Properties
Define and edit a Workflow

USERS:

Check Out a file from ORMS.
Get a file from ORMS.
Searching a file.

System Requirement Specifications

The minimum hardware and software requirements are:

Hardware Specifications

Processor : Intel Pentium based systems

Hard Disk : Minimum 500 MB of Free Space

Memory : Minimum 64 MB of RAM

Systems connected to the Local Area Network/Internet

Software Specifications

Operating system : MS Windows 2000

Database : IBM-DB2

Web Server : Tomcat Apache

Web Browser : Internet Explorer 5.0 and above

Front-End : Ajax, Java, Jsp, Servlets, jdbc, html, and java script

IDES : NetBeans 6.0

FUNCTIONAL FEATURES OF THE MODEL

As far as the project is developed the functionality is simple, the objective of the proposal is to strengthen the functioning of Audit Status Monitoring and make them effective and better. The entire scope has been classified into two streams knows as Admin Level, Traveler Level. The proposed software will cover the information needs with respect to each request of the user group viz. accepting the request, providing vulnerability document report and the current status of the audit.

INPUTS AND OUTPUTS

The major inputs and outputs and major functions of the system are follows:

Inputs:

Admin enter his user id and password for login.
Users enter his user id and password for login.
Admin adds different categories and repository managers
Administrator can edit/delete categories and repository managers also.
Administrator can check for list of users.
Users can request for files.

Outputs:

Admin can have his own home page.
Users have their own home page.
The new user’s data will be stored in the centralized database.
Admin get the search details of different criteria.
Different kind of reports is generated by administrator.
Registers details will be stored in centralized database.

PROCESS MODEL USED WITH JUSTIFICATION

ACCESS CONTROL FOR DATA WHICH REQUIRE USER AUTHENTICAION

The following commands specify access control identifiers and they are typically used to authorize and authenticate the user (command codes are shown in parentheses)

USER NAME (USER)

The user identification is that which is required by the server for access to its file system. This command will normally be the first command transmitted by the user after the control connections are made (some servers may require this).

PASSWORD (PASS)

This command must be immediately preceded by the user name command, and, for some sites, completes the user's identification for access control. Since password information is quite sensitive, it is desirable in general to "mask" it or suppress type out.

CHAPTER 2

DESIGN

SYSTEM DESIGN

Software design sits at the technical kernel of the software engineering process and is applied regardless of the development paradigm and area of application. Design is the first step in the development phase for any engineered product or system. The designer’s goal is to produce a model or representation of an entity that will later be built. Beginning, once system requirement have been specified and analyzed, system design is the first of the three technical activities -design, code and test that is required to build and verify software.

The importance can be stated with a single word “Quality”. Design is the place where quality is fostered in software development. Design provides us with representations of software that can assess for quality. Design is the only way that we can accurately translate a customer’s view into a finished software product or system. Software design serves as a foundation for all the software engineering steps that follow. Without a strong design we risk building an unstable system – one that will be difficult to test, one whose quality cannot be assessed until the last stage.

During design, progressive refinement of data structure, program structure, and procedural details are developed reviewed and documented. System design can be viewed from either technical or project management perspective. From the technical point of view, design is comprised of four activities – architectural design, data structure design, interface design and procedural design.

NORMALIZATION

It is a process of converting a relation to a standard form. The process is used to handle the problems that can arise due to data redundancy i.e. repetition of data in the database, maintain data integrity as well as handling problems that can arise due to insertion, updating, deletion anomalies.

Decomposing is the process of splitting relations into multiple relations to eliminate anomalies and maintain anomalies and maintain data integrity. To do this we use normal forms or rules for structuring relation.

Insertion anomaly: Inability to add data to the database due to absence of other data.

Deletion anomaly: Unintended loss of data due to deletion of other data.

Update anomaly: Data inconsistency resulting from data redundancy and partial update

Normal Forms: These are the rules for structuring relations that eliminate anomalies.

FIRST NORMAL FORM:

A relation is said to be in first normal form if the values in the relation are atomic for every attribute in the relation. By this we mean simply that no attribute value can be a set of values or, as it is sometimes expressed, a repeating group.

SECOND NORMAL FORM:

A relation is said to be in second Normal form is it is in first normal form and it should satisfy any one of the following rules.

1) Primary key is a not a composite primary key

2) No non key attributes are present

3) Every non key attribute is fully functionally dependent on full set of primary key.

THIRD NORMAL FORM:

A relation is said to be in third normal form if their exits no transitive dependencies.

Transitive Dependency: If two non key attributes depend on each other as well as on the primary key then they are said to be transitively dependent.

The above normalization principles were applied to decompose the data in multiple tables thereby making the data to be maintained in a consistent state.

E-R Diagram

· The relation upon the system is structure through a conceptual ER-Diagram, which not only specifics the existential entities but also the standard relations through which the system exists and the cardinalities that are necessary for the system state to continue.

· The entity Relationship Diagram (ERD) depicts the relationship between the data objects. The ERD is the notation that is used to conduct the date modeling activity the attributes of each data object noted is the ERD can be described resign a data object descriptions.

· The set of primary components that are identified by the ERD are

· Data object

· Relationships

· Attributes

· Various types of indicators.

The primary purpose of the ERD is to represent data objects and their relationships.

ER Diagram

DATA FLOW DIAGRAMS

A data flow diagram is graphical tool used to describe and analyze movement of data through a system. These are the central tool and the basis from which the other components are developed. The transformation of data from input to output, through processed, may be described logically and independently of physical components associated with the system. These are known as the logical data flow diagrams. The physical data flow diagrams show the actual implements and movement of data between people, departments and workstations. A full description of a system actually consists of a set of data flow diagrams. Using two familiar notations Yourdon, Gain and Samson notation develops the data flow diagrams. Each component in a DFD is labeled with a descriptive name. Process is further identified with a number that will be used for identification purpose. The development of DFD’S is done in several levels. Each process in lower level diagrams can be broken down into a more detailed DFD in the next level. The lop-level diagram is often called context diagram. It consists a single process bit, which plays vital role in studying the current system. The process in the context level diagram is exploded into other process at the first level DFD.

The idea behind the explosion of a process into more process is that understanding at one level of detail is exploded into greater detail at the next level. This is done until further explosion is necessary and an adequate amount of detail is described for analyst to understand the process.

Larry Constantine first developed the DFD as a way of expressing system requirements in a graphical from, this lead to the modular design.

A DFD is also known as a “bubble Chart” has the purpose of clarifying system requirements and identifying major transformations that will become programs in system design. So it is the starting point of the design to the lowest level of detail. A DFD consists of a series of bubbles joined by data flows in the system.

DFD SYMBOLS:

In the DFD, there are four symbols

1. A square defines a source(originator) or destination of system data

2. An arrow identifies data flow. It is the pipeline through which the information flows

3. A circle or a bubble represents a process that transforms incoming data flow into outgoing data flows.

4. An open rectangle is a data store, data at rest or a temporary repository of data

Process that transforms data flow.

Source or Destination of data

Data flow

Data Store

CONSTRUCTING A DFD:

Several rules of thumb are used in drawing DFD’S:

1. Process should be named and numbered for an easy reference. Each name should be representative of the process.

2. The direction of flow is from top to bottom and from left to right. Data traditionally flow from source to the destination although they may flow back to the source. One way to indicate this is to draw long flow line back to a source. An alternative way is to repeat the source symbol as a destination. Since it is used more than once in the DFD it is marked with a short diagonal.

3. When a process is exploded into lower level details, they are numbered.

4. The names of data stores and destinations are written in capital letters. Process and dataflow names have the first letter of each work capitalized.

A DFD typically shows the minimum contents of data store. Each data store should contain all the data elements that flow in and out.

Questionnaires should contain all the data elements that flow in and out. Missing interfaces redundancies and like is then accounted for often through interviews.

SAILENT FEATURES OF DFD’S

1. The DFD shows flow of data, not of control loops and decision are controlled considerations do not appear on a DFD.

2. The DFD does not indicate the time factor involved in any process whether the dataflow take place daily, weekly, monthly or yearly.

3. The sequence of events is not brought out on the DFD.

TYPES OF DATA FLOW DIAGRAMS

1. Current Physical

2. Current Logical

3. New Logical

4. New Physical

CURRENT PHYSICAL:

In Current Physical DFD process label include the name of people or their positions or the names of computer systems that might provide some of the overall system-processing label includes an identification of the technology used to process the data. Similarly data flows and data stores are often labels with the names of the actual physical media on which data are stored such as file folders, computer files, business forms or computer tapes.

CURRENT LOGICAL:

The physical aspects at the system are removed as much as possible so that the current system is reduced to its essence to the data and the processors that transforms them regardless of actual physical form.

NEW LOGICAL:

This is exactly like a current logical model if the user were completely happy with the user were completely happy with the functionality of the current system but had problems with how it was implemented typically through the new logical model will differ from current logical model while having additional functions, absolute function removal and inefficient flows recognized.

NEW PHYSICAL:

The new physical represents only the physical implementation of the new system.

RULES GOVERNING THE DFD’S

PROCESS

1) No sprocess can have only outputs.

2) No process can have only inputs. If an object has only inputs than it must be a sink.

3) A process has a verb phrase label.

DATA STORE

1) Data cannot move directly from one data store to another data store, a process must move data.

2) Data cannot move directly from an outside source to a data store, a process, which receives, must move data from the source and place the data into data store

3) A data store has a noun phrase label.

SOURCE OR SINK

The origin and /or destination of data.

1) Data cannot move direly from a source to sink it must be moved by a process

2) A source and /or sink has a noun phrase land

DATA FLOW

1) A Data Flow has only one direction of flow between symbols. It may flow in both directions bestween a process and a data store to show a read before an update. The later is usually indicated however by two separate arrows since these happen at different type.

2) A join in DFD means that exactly the same data comes from any of two or more different processes data store or sink to a common location.

3) A data flow cannot go directly back to the same process it leads. There must be at least one other process that handles the data flow produce some other data flow returns the original data into the beginning process.

4) As Data flow to a data store means update (delete or change).

5) A data Flow from a data store means retrieve or use.

A data flow has a noun phrase label more than one data flow noun phrase can appear on a single arrow as long as all of the flows on the same arrow move together as one package.

DFD Diagrams:

Context Level (0^th Level) DFD

Login DFD Diagram

LOGIN MASTER

YES YES

Admin DFD Diagram

Admin Dataflow Diagram

User DFD Diagram

User Dataflow Diagram

UML DIAGRAMS

Admin Use Case Diagram

Admin Use Case Diagram

User Use case Diagram

User Use case Diagram

RepositorymanagerUsecaseDiagram

Repository manager Use case Diagram

Class Diagram

Sequence diagram for user

Sequence diagram for user

Sequence diagram for admin

Sequence diagram for admin

Sequence diagram for repository manager

Sequence diagram for repository manager

Activity diagram

CHAPTER 3

IMPLEMENTATION

FEASIBILITY REPORT

Preliminary investigation examine project feasibility, the likelihood the system will be useful to the organization. The main objective of the feasibility study is to test the Technical, Operational and Economical feasibility for adding new modules and debugging old running system. All system is feasible if they are unlimited resources and infinite time. There are aspects in the feasibility study portion of the preliminary investigation:

· Technical Feasibility

· Operational Feasibility

· Economical Feasibility

TECHNICAL FEASIBILITY

The technical issue usually raised during the feasibility stage of the investigation includes the following:

· Does the necessary technology exist to do what is suggested?

· Do the proposed equipments have the technical capacity to hold the data required to use the new system?

· Will the proposed system provide adequate response to inquiries, regardless of the number or location of users?

· Can the system be upgraded if developed?

· Are there technical guarantees of accuracy, reliability, ease of access and data security?

Earlier no system existed to cater to the needs of ‘Secure Infrastructure Implementation System’. The current system developed is technically feasible. It is a web based user interface for audit workflow at NIC-CSD. Thus it provides an easy access to the users. The database’s purpose is to create, establish and maintain a workflow among various entities in order to facilitate all concerned users in their various capacities or roles. Permission to the users would be granted based on the roles specified. Therefore, it provides the technical guarantee of accuracy, reliability and security. The software and hard requirements for the development of this project are not many and are already available in-house at NIC or are available as free as open source. The work for the project is done with the current equipment and existing software technology. Necessary bandwidth exists for providing a fast feedback to the users irrespective of the number of users using the system.

OPERATIONAL FEASIBILITY

Proposed projects are beneficial only if they can be turned out into information system. That will meet the organization’s operating requirements. Operational feasibility aspects of the project are to be taken as an important part of the project implementation. Some of the important issues raised are to test the operational feasibility of a project includes the following:

· Is there sufficient support for the management from the users?

· Will the system be used and work properly if it is being developed and implemented?

· Will there be any resistance from the user that will undermine the possible application benefits?

This system is targeted to be in accordance with the above-mentioned issues. Beforehand, the management issues and user requirements have been taken into consideration. So there is no question of resistance from the users that can undermine the possible application benefits.

The well-planned design would ensure the optimal utilization of the computer resources and would help in the improvement of performance status.

ECONOMICAL FEASIBILITY

A system can be developed technically and that will be used if installed must still be a good investment for the organization. In the economical feasibility, the development cost in creating the system is evaluated against the ultimate benefit derived from the new systems. Financial benefits must equal or exceed the costs. The system is economically feasible. It does not require any addition hardware or software. Since the interface for this system is developed using the existing resources and technologies available at NIC, There is nominal expenditure and economical feasibility for certain.

SOFTWARE REQUIREMENT SPECIFICAION

INTRODUCTION

Purpose: The main purpose for preparing this document is to give a general insight into the analysis and requirements of the existing system or situation and for determining the operating characteristics of the system.

Scope: This Document plays a vital role in the development life cycle (SDLC) and it describes the complete requirement of the system. It is meant for use by the developers and will be the basic during testing phase. Any changes made to the requirements in the future will have to go through formal change approval process.

DEVELOPERS RESPONSIBILITIES OVERVIEW:

The developer is responsible for:

· Developing the system, which meets the SRS and solving all the requirements of the system?

· Demonstrating the system and installing the system at client's location after the acceptance testing is successful.

· Submitting the required user manual describing the system interfaces to work on it and also the documents of the system.

· Conducting any user training that might be needed for using the system.

· Maintaining the system for a period of one year after installation.

FUNCTIONAL REQUIREMENTS

OUTPUT DESIGN

Outputs from computer systems are required primarily to communicate the results of processing to users. They are also used to provide a permanent copy of the results for later consultation. The various types of outputs in general are:

· External Outputs, whose destination is outside the organization,.

· Internal Outputs whose destination is within organization and they are the

· User’s main interface with the computer.

· Operational outputs whose use is purely within the computer department.

· Interface outputs, which involve the user in communicating directly.

Output Media:

In the next stage it is to be decided that which medium is the most appropriate for the output. The main considerations when deciding about the output media are:

The suitability for the device to the particular application.
The need for a hard copy.
The response time required.
The location of the users
The software and hardware available.

Keeping in view the above description the project is to have outputs mainly coming under the category of internal outputs. The main outputs desired according to the requirement specification are:

The outputs were needed to be generated as a hot copy and as well as queries to be viewed on the screen. Keeping in view these outputs, the format for the output is taken from the outputs, which are currently being obtained after manual processing. The standard printer is to be used as output media for hard copies.

INPUT DESIGN

Input design is a part of overall system design. The main objective during the input design is as given below:

· To produce a cost-effective method of input.

· To achieve the highest possible level of accuracy.

· To ensure that the input is acceptable and understood by the user.

INPUT STAGES:

The main input stages can be listed as below:

· Data recording

· Data transcription

· Data conversion

· Data verification

· Data control

· Data transmission

· Data validation

· Data correction

INPUT TYPES:

It is necessary to determine the various types of inputs. Inputs can be categorized as follows:

· External inputs, which are prime inputs for the system.

· Internal inputs, which are user communications with the system.

· Operational, which are computer department’s communications to the system?

· Interactive, which are inputs entered during a dialogue.

INPUT MEDIA:

At this stage choice has to be made about the input media. To conclude about the input media consideration has to be given to;

· Type of input

· Flexibility of format

· Speed

· Accuracy

· Verification methods

· Rejection rates

· Ease of correction

· Storage and handling requirements

· Security

· Easy to use

· Portability

Keeping in view the above description of the input types and input media, it can be said that most of the inputs are of the form of internal and interactive.

As Input data is to be the directly keyed in by the user, the keyboard can be considered to be the most suitable input device.

ERROR AVOIDANCE

At this stage care is to be taken to ensure that input data remains accurate form the stage at which it is recorded up to the stage in which the data is accepted by the system. This can be achieved only by means of careful control each time the data is handled.

ERROR DETECTION

Even though every effort is make to avoid the occurrence of errors, still a small proportion of errors is always likely to occur, these types of errors can be discovered by using validations to check the input data.

DATA VALIDATION

Procedures are designed to detect errors in data at a lower level of detail. Data validations have been included in the system in almost every area where there is a possibility for the user to commit errors. The system will not accept invalid data. Whenever an invalid data is keyed in, the system immediately prompts the user and the user has to again key in the data and the system will accept the data only if the data is correct. Validations have been included where necessary.

The system is designed to be a user friendly one. In other words the system has been designed to communicate effectively with the user. The system has been designed with popup menus.

USER INTERFACE DESIGN

It is essential to consult the system users and discuss their needs while designing the user interface:

USER INTERFACE SYSTEMS CAN BE BROADLY CLASIFIED AS:

1. User initiated interface the user is in charge, controlling the progress of the user/computer dialogue. In the computer-initiated interface, the computer selects the next stage in the interaction.

2. Computer initiated interfaces

In the computer initiated interfaces the computer guides the progress of the user/computer dialogue. Information is displayed and the user response of the computer takes action or displays further information.

USER_INITIATED INTERFACES

User initiated interfaces fall into tow approximate classes:

1. Command driven interfaces: In this type of interface the user inputs commands or queries which are interpreted by the computer.

2. Forms oriented interface: The user calls up an image of the form to his/her screen and fills in the form. The forms oriented interface is chosen because it is the best choice.

COMPUTER-INITIATED INTERFACES

The following computer – initiated interfaces were used:

1. The menu system for the user is presented with a list of alternatives and the user chooses one; of alternatives.

2. Questions – answer type dialog system where the computer asks question and takes action based on the basis of the users reply.

Right from the start the system is going to be menu driven, the opening menu displays the available options. Choosing one option gives another popup menu with more options. In this way every option leads the users to data entry form where the user can key in the data.

ERROR MESSAGE DESIGN:

The design of error messages is an important part of the user interface design. As user is bound to commit some errors or other while designing a system the system should be designed to be helpful by providing the user with information regarding the error he/she has committed.

This application must be able to produce output at different modules for different inputs.

PERFORMANCE REQUIREMENTS

Performance is measured in terms of the output provided by the application.

Requirement specification plays an important part in the analysis of a system. Only when the requirement specifications are properly given, it is possible to design a system, which will fit into required environment. It rests largely in the part of the users of the existing system to give the requirement specifications because they are the people who finally use the system. This is because the requirements have to be known during the initial stages so that the system can be designed according to those requirements. It is very difficult to change the system once it has been designed and on the other hand designing a system, which does not cater to the requirements of the user, is of no use.

The requirement specification for any system can be broadly stated as given below:

· The system should be able to interface with the existing system

· The system should be accurate

· The system should be better than the existing system

The existing system is completely dependent on the user to perform all the duties.

SURVEY OF TECHNOLOGIES

INTRODUCTION TO JAVA

Initially the language was called as “oak” but it was renamed as “JAVA” in 1995. The primary motivation of this language was the need for a platform-independent (i.e., architecture neutral) language that could be used to create software to be embedded in various customer electronic devices.

Ø Java is a programmers language

Ø Java is cohesive and consistent

Ø Except for those constraints imposed by the internet environment , java gives the programmer, full control

Finally, Java is to internet programming where C was to system programming.

Importance of Java to the Internet:

Java has had a profound effect on the internet. This is because of; java expands the universe of objects that can move about freely in cyber space. In a network, two categories of objects are transmitted between the server and the computer. They are: Passive information and Dynamic Active Programs. The dynamic, self executing programs cause serious problems in the areas of security and probability. But, java addresses those concerns and by doing so, has opened the door to an exciting new form of program called the Applet.

Java Can Be Used To Create Two Types Of Programs:

Applications and Applets:

An application is a program that runs on our computer under the operating system of the computer. It is more or less like one creating using C or C++. Java’s ability to create Applets makes it important. An Applet is an application designed to be transmitted over the internet and executed by a java-compatible web browser. An Applet is actually a tiny java program, dynamically downloaded across the network just like a image. But the difference is, it is an intelligent program, not just a media file. It can react to the user input and dynamically change.

INTRODUCTION TO SERVLETS:

Introduction:

The java web server is java soft’s own web server. The java web server is just a part of a frame work, intended to provide you not just with the web server, but also with tools. To built customized network servers for any internet or intranet flying server system. Servlets are to a web server, how applets are to the browser.

About Servlets:

Servlets provide a java based solution used to address the problems currently associated with doing server –side programming including inextensible scripting solutions, platforms specific APIS, and incomplete interfaces.

Servlets are objects that conform to a specific interface that can be plugged into a java based server. Servlets are to the server-slide what applets are to the client-side-object byte codes that can be dynamically loaded off the net. They differ from applets in that they are face less objects (without graphics or GUI components). They serve as platform independent, dynamically loadable, pluggable, helper byte code objects on the server side objects that can be used dynamically extend server side functionality.

For example, an HTTP Servlets ca be used to generate dynamic HTML content. Then you use served to do dynamic content to get the following advantages:

Ø They are faster and cleaner than CGI scripts

Ø They use a standard API ( the Servlets API)

Ø They provide all the advantages of java ( run on a variety of servers without needing to be rewritten)

Attractiveness of Servlets:

There are many features of Servlets that make them easy and attractive to use. This includes:

Ø Easily configured using the GUI-based admin tool.

Ø Can be loaded and invoked from a local disk or remotely across the network.

Ø Can be linked together, or chained , so that one Servlets can call another Servlets, or several Servlets in sequence

Ø Can be called dynamically from within HTML pages, using server-side include tax

Ø Are secure-even when downloading across the network, the Servlets security model and Servlets sand box protect your system from unfriendly behavior.

Advantages of the servlet API:

One of the great advantages of the servlet API is the protocol independence. It assumes nothing about:

Ø The protocol being used to transmit on the net

Ø How it is loaded

Ø The server environment it will be running in.

These qualities are important, because it allows the servlet API to be embedded in many different kinds of servers. There are other advantages to the servlet API as well. These include:

Ø It is extensible-you can inherit all your functionality from the base classes made available to you.

Ø It is simple , small and easy to use

Features of Servlets:

Ø Servlets are persistent. Servlets are loaded only why the web server and can maintain services between requests.

Ø Servlets are fast. Since servlet only need to be loaded once, they offer much better performance over their CGI counterparts

Ø Servlet are platform independent.

Ø Servlet are extensible. Java is a low bust; object oriented programming language which easily can be extended to suit your needs.

Ø Servlets are secure.

Ø Servlets can be used with a variety of clients.

INTRODUCTION TO JAVA SCRIPT

JavaScript is a script-based programming language that was developed by Netscape Communication Corporation. JavaScript was originally called Live Script and renamed as JavaScript to indicate this relationship with java. JavaScript supports the development of both client and server components of web based application. On the client side, it can be used to write web server program that can process information submitted by a web browser and updates the browser’s display accordingly.

Even though JavaScript supports both client and server web programming, be prefer JavaScript at client side programming since most of the browsers support it. JavaScript is almost as easy to learn as HTML, and JavaScript statements can be included in HTML documents by enclosing the statements between a pair of scripting tags.

<SCRIPTS>...</SCRIPT>.

<SCRIPT LANGUAGE=” JavaScript”

JavaScript statements

</SCRIPT>

Here are a few things we can do with JavaScript:

Ø Validate the contents of a form and make calculations.

Ø Add scrolling or changing messages to the browser’s status line

Ø Animate images or rotate images can change when we move the mouse over them.

Ø Detect the browser in use and display different content for different browser.

Ø Detect installed plug-ins and notify the user if a plug-ins is required

We can do much more with JavaScript, including creating entire application

JavaScript Vs Java

JavaScript and java are entirely different languages. A few of the glaring differences are :

Ø Java applets are generally displayed in a box within the web document; JavaScript can affect any part of the web document itself.

Ø While JavaScript is best suited to simple application and adding interacting features to web pages: java can be use for incredibly complex applications

There are many other differences but the important thing to remember is that JavaScript and java are separate languages. They are both useful for different things; in fact they can be used together to combine their advantages.

Advantages:

Ø JavaScript can be used for server-side and client-side scripting.

Ø It is more flexible than VB script.

Ø JavaScript is the default scripting languages at client side since all the browsers supports it.

INTRODUCTION TO HTML

Hypertext Markup Language (HTML), the languages of the World Wide Web (WWW), allows users to produces Web pages that include text, graphics and pointer to other Web pages (Hyperlinks).

HTML is not a programming language but it is an application of ISO Standard 8879, SGML (Standard Generalized Markup Language), but specialized to hypertext and adapted to the web. The idea behind Hypertext is that instead of reading text in rigid linear structure, we can easily jump from one point to another point. We can navigate through the information based on our interest and preference.

A markup language is simply a series of elements, each delimited with special characters that define how text or other items enclosed within the elements should be displayed. Hyperlinks are underlined or emphasized works that load to other documents or some portions of the same document.

HTML can be used to display any type of document on the host computer, which can be geographically at a different location. It is a versatile language and can be used on any platform or desktop.

HTML provides tags (special codes) to make the document look attractive. HTML tags are not case-sensitive. Using graphics, fonts, different sizes, color, etc., can enhance the presentation of the document. Anything that is not a tag is part of the document itself.

INTRODUCTION TO JSP

Java server pages are a simple, yet powerful technology for creating and maintaining dynamic –content web pages. Based on the Java programming language, Java Server Pages offers proven portability, open standards, and a mature reusable component model. The JSP architecture enables the separation of content generation from content presentation. This separation not eases maintenance headaches; it also allows web team members to focus on their areas of expertise. Now, web page designer can concentrate on layout, and web application designers on programming, with minimal concern about impacting each other’s work.

Features of JSP

Portability:

Java server pages files can be run on any web server or web-enabled application server that provides support for them. Dubbed the JSP engine, this support involves recognition, translation, and management of the Java Server Page lifecycle and its interaction components.

Components:

It was mentioned earlier that the Java Server Pages architecture can include reusable Java components. The architecture also allows for the embedding of a scripting language directly into the Java server Pages file. The components current supported include java Beans, and servlet.

Processing:

A Java server Pages file is essentially an HTML document with JSP scripting or tags. The Java server Pages file has a JSP extension to the server as a Java Server Pages file. Before the pages served, the Java server Pages syntax is parsed and processed into a servlet on a server side. The servlet that is generated outputs real content in straight HTML for responding to the client.

Access models

A Java Server Pages file may be accessed in at least two different ways. A client’s request comes directly into a Java Server Page. In this scenario, suppose the page accesses reusable Java Bean component that performs particular well-defined computations like accessing a database. The result of the Beans computations, called result sets is stored within the Bean as properties. The page uses such Beans to generate dynamic content and present it back to the client.

In both of the above cases, the page could also contain any valid Java code. Java Server Pages architecture encourages separation of content from presentation

Steps in the execution of a JSP application:

1. The client sends a request to the web server for a JSP file by giving the name of the JSP file with in the form tag of a HTML page.

2. This request is transferred to the Java web server. At the server side Java web server receives the request and if it is a request for a JSP file server gives this request to the JSP engine.

In this scenario, suppose the page accesses reusable JavaBeans components that perform particular well-defined computations like accessing a database. The result of the Bean’s computations, called result sets are stored within the Bean as properties. The page uses such Beans to generate dynamic content and present it back to the client.

A request comes through a servlet.

· The servlet generates the dynamic content. To handle the response to the client, the servlet creates a Bean and stores the dynamic content (sometimes called the result set) in the Bean. The servlet then invokes a Java Server Page that will present the content along with the Bean containing the generated from the servlet.

· There are two APIs to support this model of request processing using Java Server Pages. One API facilitates passing context between the invoking servlet and the Java Server Page. The other API lets the invoking servlet specify which Java Server Page to use.

· In both of the above cases, the page could also contain any valid Java code. The Java Server Pages architecture encourages separation of content from presentation—it does not mandate it.

How to Choose Between Access Models

With at least two access models, the question naturally arises “When does it make sense to have a Java Server Page as the front-end to a servlet, as the back-end to a servlet, or use only the servlet? Here are some possible guidelines:

If a graphical interface (GUI) is necessary to collect the request data—use a Java Server Pagesfile.

If the request and request parameters are otherwise available to the servlet, but the results of the servlet processing requires a graphical interface to present them—use a JavaServerPagesfile.
If presentation layout is minimal (will not require very many println lines in your servlet code) and you don’t need to make that presentation logic available to a customer or your webpage designer, then a Servlet might suffice.

Using XML with JSP:

The Java Server Pages technology is an ideal way to describe processing of XML input and output. Simple XML generation can be done by just writing the XML as static template portions within the JSP. Dynamic generation will be done through JavaBeans components, Enterprise JavaBeans components, or via custom tags that generate XML. Similarly, input XML can be received from POST or QUERY arguments and then sent directly to JavaBeans components, Enterprise JavaBeans components, or custom tags, or manipulated via the scripting.

There are two attributes of JSP that make it especially suited for describing XML processing. One is that XML fragments can be described directly in the JSP text either as templates for input into some XML-consuming component or as templates for output to be extended with some other XML fragments. Another attribute is that the tag extension mechanism enables the creation of specific tags and directives that are targeted at useful XML manipulation operations.

Future versions of the JSP specification will include several standard tags that will support XML manipulation, including the transformation of the XML produced by the given JSP using XTL/XSL.

Features in JSP 1.0

The JSP 1.0 specification has mandatory and optional features. JSP 1.0 enables a tag extension mechanism for the creation of custom tags but such a mechanism will not appear until a later version of the specification.

The JSP 1.0 specification includes:

JSP standard directives
JSP standard actions
Script language declarations, Scrip lets and expressions

A future version of the JSP specification will also add additional features to enhance the use of JSP pages in a J2EE platform.

JAVA BEANS

The JavaBeans API makes it possible to write component software in the Java programming language. Components are self-contained, reusable software units that can be visually composed into composite components, applets, applications, and Servlets using visual application builder tools. JavaBeans components are known as Beans.

Components expose their features (for example, public methods and events) to builder tools for visual manipulation. A Bean’s features are exposed because feature names adhere to specific design patterns. A “JavaBeans-enabled” builder tool can then examines the Bean’s patterns, discern its features, and expose those features for visual manipulation. A builder tool maintains Beans in a palette or toolbox. You can select a Bean from the toolbox, drop it into a form, modify its appearance and behavior, define its interaction with other Beans, and compose it and other Beans into an applet, application, or new Bean. All this can be done without writing a line of code.

Although Beans are designed to be understood by builder tools, all key APIs, including support for events, properties, and persistence, have been designed to be easily read and understood by human programmers as well.

New JavaBeans Features

Here is a list of upcoming Beans and Beans-related features:

The Java Activation Framework (JAF). The JAF is a data typing and command registry API. With the JAF you can discover an arbitrary data object’s type, and look command applications or Beans that can process that data type, and activate the appropriate command at a user gesture. For example, a browser can identify a file’s data type, and then launch the appropriate plug-in to view or edit the file. The JAF is a Java standard extension.

The Extensible Runtime Containment and Services Protocol, also known as

bean
context

. Previous to this protocol a Bean only knew about and had access to the Java Virtual Machine (JVM) in which the Bean ran, and the core Java APIs. This protocol introduces a standard way for a Bean to discover and access attributes or services provided by a Bean’s enclosing environment, and for a Bean’s enclosing environment to discover and access a Bean’s attributes and services. The java.beans.beancontext API introduces the ability to nest Beans and Bean contexts within a hierarchical structure. At runtime, a Bean can obtain services from its containing environment; the Bean can then make use of those services, or propagate those services to any Beans that it contains.

Drag and Drop Support. The java.awt.dnd API provides support for drag and drop between Java applications and native platform applications.

Databases and the Web

Databases are at the root of all business computing today. At some point, you are going to want to integrate a company database with your Web site. On the other hand, perhaps when you have a large Web site, you will want to create a database that will let clients search the text of your HTML files.

There are several ways to achieve database and Web site integration. Let’s concentrate on ODBC and JDBC.

ODBC

Microsoft Open Database Connectivity (ODBC) is a standard programming interface for application developers and database systems providers. Before ODBC became a de facto standard for Windows programs to interface with database systems, programmers had to use proprietary languages for each database they wanted to connect to. Now, ODBC has made the choice of the database system almost irrelevant from a coding perspective, which is as it should be. Application developers have much more important things to worry about than the syntax that is needed to port their program from one database to another when business needs suddenly change.

Through the ODBC Administrator in Control Panel, you can specify the particular database that is associated with a data source that an ODBC application program is written to use. Think of an ODBC data source as a door with a name on it. Each door will lead you to a particular database. For example, the data source named Sales Figures might be a Db2 database, whereas the Accounts Payable data source could refer to an Access database. The physical database referred to by a data source can reside anywhere on the LAN.

The ODBC system files are not installed on your system by Windows 95. Rather, they are installed when you setup a separate database application, such as Db2 Client or Visual Basic 4.0. When the ODBC icon is installed in Control Panel, it uses a file called ODBCINST.DLL. It is also possible to administer your ODBC data sources through a stand-alone program called ODBCADM.EXE. There is a 16-bit and a 32-bit version of this program and each maintains a separate list of ODBC data sources.

From a programming perspective, the beauty of ODBC is that the application can be written to use the same set of function calls to interface with any data source, regardless of the database vendor. The source code of the application doesn’t change whether it talks to Oracle or Db2. We only mention these two as an example. There are ODBC drivers available for several dozen popular database systems. Even Excel spreadsheets and plain text files can be turned into data sources. The operating system uses the Registry information written by ODBC Administrator to determine which low-level ODBC drivers are needed to talk to the data source (such as the interface to Oracle or Db2). The loading of the ODBC drivers is transparent to the ODBC application program. In a client/server environment, the ODBC API even handles many of the network issues for the application programmer.

The advantages of this scheme are so numerous that you are probably thinking there must be some catch. The only disadvantage of ODBC is that it isn’t as efficient as talking directly to the native database interface. ODBC has had many detractors make the charge that it is too slow. Microsoft has always claimed that the critical factor in performance is the quality of the driver software that is used. In our humble opinion, this is true. The availability of good ODBC drivers has improved a great deal recently. And anyway, the criticism about performance is somewhat analogous to those who said that compilers would never match the speed of pure assembly language. Maybe not, but the compiler (or ODBC) gives you the opportunity to write cleaner programs, which means you finish sooner. Meanwhile, computers get faster every year.

JDBC:

In an effort to set an independent database standard API for Java; Sun Microsystems developed Java Database Connectivity, or JDBC. JDBC offers a generic SQL database access mechanism that provides a consistent interface to a variety of RDBMSs. This consistent interface is achieved through the use of “plug-in” database connectivity modules, or drivers. If a database vendor wishes to have JDBC support, he or she must provide the driver for each platform that the database and Java run on.

To gain a wider acceptance of JDBC, Sun based JDBC’s framework on ODBC. As you discovered earlier in this chapter, ODBC has widespread support on a variety of platforms. Basing JDBC on ODBC will allow vendors to bring JDBC drivers to market much faster than developing a completely new connectivity solution.

JDBC was announced in March of 1996. It was released for a 90 day public review that ended June 8, 1996. Because of user input, the final JDBC v1.0 specification was released soon after.

The remainder of this section will cover enough information about JDBC for you to know what it is about and how to use it effectively. This is by no means a complete overview of JDBC. That would fill an entire book.

JDBC Goals:

Few software packages are designed without goals in mind. JDBC is one that, because of its many goals, drove the development of the API. These goals, in conjunction with early reviewer feedback, have finalized the JDBC class library into a solid framework for building database applications in Java.

The goals that were set for JDBC are important. They will give you some insight as to why certain classes and functionalities behave the way they do. The eight design goals for JDBC are as follows:

1. SQL Level API

The designers felt that their main goal was to define a SQL interface for Java. Although not the lowest database interface level possible, it is at a low enough level for higher-level tools and APIs to be created. Conversely, it is at a high enough level for application programmers to use it confidently. Attaining this goal allows for future tool vendors to “generate” JDBC code and to hide many of JDBC’s complexities from the end user.

SQLConformance
SQL syntax varies as you move from database vendor to database vendor. In an effort to support a wide variety of vendors, JDBC will allow any query statement to be passed through it to the underlying database driver. This allows the connectivity module to handle non-standard functionality in a manner that is suitable for its users.
JDBC must be implemental on top of common database interfaces

The JDBC SQL API must “sit” on top of other common SQL level APIs. This goal allows JDBC to use existing ODBC level drivers by the use of a software interface. This interface would translate JDBC calls to ODBC and vice versa.

Provide a Java interface that is consistent with the rest of the Java system
Because of Java’s acceptance in the user community thus far, the designers feel that they should not stray from the current design of the core Java system.
Keep it simple

This goal probably appears in all software design goal listings. JDBC is no exception. Sun felt that the design of JDBC should be very simple, allowing for only one method of completing a task per mechanism. Allowing duplicate functionality only serves to confuse the users of the API.

Use strong, static typing wherever possible

Strong typing allows for more error checking to be done at compile time; also, less errors appear at runtime.

Keep the common cases simple

Because more often than not, the usual SQL calls used by the programmer are simple SELECT’s, INSERT’s, DELETE’s and UPDATE’s, these queries should be simple to perform with JDBC. However, more complex SQL statements should also be possible.

During an Db2 Database design project, the analysis of your business needs identifies all the fields or attributes of interest. If your business needs change over time, you define any additional fields or change the definition of existing fields.

PRIMARY KEY

Every table in DB2 has a field or a combination of fields that uniquely identifies each record in the table. The Unique identifier is called the Primary Key, or simply the Key. The primary key provides the means to distinguish one record from all other in a table. It allows the user and the database system to identify, locate and refer to one particular record in the database.

RELATIONAL DATABASE

Sometimes all the information of interest to a business operation can be stored in one table. Db2 makes it very easy to link the data in multiple tables. Matching an employee to the department in which they work is one example. This is what makes Db2 a relational database management system, or RDBMS. It stores data in two or more tables and enables you to define relationships between the table and enables you to define relationships between the tables.

FOREIGN KEY

REFERENTIAL INTEGRITY

Not only does Db2 allow you to link multiple tables, it also maintains consistency between them. Ensuring that the data among related tables is correctly matched is referred to as maintaining referential integrity.

DATA ABSTRACTION

Physical level: This is the lowest level of abstraction at which one describes how the data are actually stored.

Conceptual Level: At this level of database abstraction all the attributed and what data are actually stored is described and entries and relationship among them.

View level: This is the highest level of abstraction at which one describes only part of the database.

ADVANTAGES OF RDBMS

· Redundancy can be avoided

· Inconsistency can be eliminated

· Data can be Shared

· Standards can be enforced

· Security restrictions ca be applied

· Integrity can be maintained

· Conflicting requirements can be balanced

· Data independence can be achieved.

DISADVANTAGES OF DBMS

FEATURES OF DB2 (RDBMS)

DB2 is one of the leading database management systems (DBMS) because it is the only Database that meets the uncompromising requirements of today’s most demanding information systems. From complex decision support systems (DSS) to the most rigorous online transaction processing (OLTP) application, even application that require simultaneous DSS and OLTP access to the same critical data, Db2 leads the industry in both performance and capability.

DB2 is a truly portable, distributed, and open DBMS that delivers unmatched performance, continuous operation and support for every database.

DB2 RDBMS is high performance fault tolerant DBMS which is specially designed for online transactions processing and for handling large database application.

DB2 with transactions processing option offers two features which contribute to very high level of transaction processing throughput, which are

· The row level lock manager

ENTERPRISE WIDE DATA SHARING

The unrivaled portability and connectivity of the DB2 DBMS enables all the systems in the organization to be linked into a singular, integrated computing resource.

PORTABILITY

DB2 is fully portable to more than 80 distinct hardware and operating systems platforms, including UNIX, MSDOS, OS/2, Macintosh and dozens of proprietary platforms. This portability gives complete freedom to choose the database server platform that meets the system requirements.

OPEN SYSTEMS

DB2 offers a leading implementation of industry –standard SQL. Db2’s open architecture integrates DB2 and non –DB2 DBMS with industry’s most comprehensive collection of tools, application, and third party software products Db2’s Open architecture provides transparent access to data from other relational database and even non-relational database.

DISTRIBUTED DATA SHARING

Db2’s networking and distributed database capabilities to access data stored on remote server with the same ease as if the information was stored on a single local computer. A single SQL statement can access data at multiple sites. You can store data where system requirements such as performance, security or availability dictate.

UNMATCHED PERFORMANCE

The most advanced architecture in the industry allows the DB2 DBMS to deliver unmatched performance.

SOPHISTICATED CONCURRENCY CONTROL

Real World applications demand access to critical data. With most database Systems application becomes “contention bound” – which performance is limited not by the CPU power or by disk I/O, but user waiting on one another for data access. Db2 employs full, unrestricted row-level locking and contention free queries to minimize and in many cases entirely eliminates contention wait times.

NO I/O BOTTLENECKS

Db2’s fast commit groups commit and deferred write technologies dramatically reduce disk I/O bottlenecks. While some database write whole data block to disk at commit time, Db2 commits transactions with at most sequential log file on disk at commit time, On high throughput systems, one sequential writes typically group commit multiple transactions. Data read by the transaction remains as shared memory so that other transactions may access that data without reading it again from disk.

CHAPTER 4

EXPERIMENTATION

CODING

<%--

Document : homepage

--%>

<%@ page language="java" %>

<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">

<head>

<title>Online Reusable Components Repository Search Engine</title>

function ChkEmailField(F,T){

var val = F.value;

var pattern = /^\w+([\.-]?\w+)*@\w+([\.-]?\w+)*(\.\w{2,3})+$/;

if(!(pattern.test(val)==true)){alert("Please enter valid email for "+T);

F.focus();

return false;}

return true;

}

function validateNumber(x){

var ed=x.value;

var pattern = /^([0-9]{6})$/;

if(!pattern.test(ed)==true)

{alert( x.name.substring(3) + "must be number");

x.focus();

return false;

}

else

{return true;

}

} function validateNumber1(x){

var ed=x.value;

var pattern = /^([0-9]{10})$/;

if(!pattern.test(ed)==true)

{alert( x.name.substring(3) + "must be number");

x.focus();

return false;

}

else

{return true;

}

}function ChkMandatoryField(F,T){

var val= F.value;

if(val==""){

alert(T+" is mandatory");

F.focus();

return false;

}

return true;

}

function validate()

{

var frm =document.forms[0];

if(ChkMandatoryField(frm.txtuserid,"userid")==false) return false;

if(ChkMandatoryField(frm.txtpass,"pass")==false) return false;

if(ChkMandatoryField(frm.txtfirstname,"firstname")==false) return false;

if(ChkMandatoryField(frm.txtlastname,"lastname")==false) return false;

if(ChkMandatoryField(frm.txtadds,"adds")==false) return false;

if(ChkMandatoryField(frm.txtstreet,"street")==false) return false;

if(ChkMandatoryField(frm.txtcity,"city")==false) return false;

if(ChkMandatoryField(frm.txtstate,"state")==false) return false;

if(ChkMandatoryField(frm.txtcountry,"country")==false) return false;

if(ChkMandatoryField(frm.txtpin,"pin")==false) return false;

if(validateNumber(frm.txtpin)==false) return false;

if(ChkMandatoryField(frm.txtcell,"cell")==false) return false;

if(validateNumber1(frm.txtcell)==false) return false;

if(ChkMandatoryField(frm.txtemail,"email")==false) return false;

if(ChkEmailField(frm.txtemail,"EMAIL")==false)return false;

}

</script>

</head>

<ul>

</ul>

</div>

<h1><a href="#">Component Repository</a></h1>

</div>

<ul>

<li class="first"><a href="#" title="">Register</a></li>

<li><a href="Login.jsp" title="">Login</a></li>

<li><a href="ListComponent.jsp" title="">ListComponent</a></li>

<li><a href="DeleteComponent.jsp" title="">Delete</a></li>

<li><a href="searc

SYSTEM TESTING

Software testing is a critical element of software quality assurance and represents the ultimate review of specification, design and coding. In fact, testing is the one step in the software engineering process that could be viewed as destructive rather than constructive.

A strategy for software testing integrates software test case design methods into a well-planned series of steps that result in the successful construction of software. Testing is the set of activities that can be planned in advance and conducted systematically. The underlying motivation of program testing is to affirm software quality with methods that can economically and effectively apply to both strategic to both large and small-scale systems. STRATEGIC APPROACH TO SOFTWARE TESTING

The software engineering process can be viewed as a spiral. Initially system engineering defines the role of software and leads to software requirement analysis where the information domain, functions, behavior, performance, constraints and validation criteria for software are established. Moving inward along the spiral, we come to design and finally to coding. To develop computer software we spiral in along streamlines that decrease the level of abstraction on each turn.

A strategy for software testing may also be viewed in the context of the spiral. Unit testing begins at the vertex of the spiral and concentrates on each unit of the software as implemented in source code. Testing progress by moving outward along the spiral to integration testing, where the focus is on the design and the construction of the software architecture. Talking another turn on outward on the spiral we encounter validation testing where requirements established as part of software requirements analysis are validated against the software that has been constructed. Finally we arrive at system testing, where the software and other system elements are tested as a whole.

FIG 9.1 Testing Methods Diagram

UNIT TESTING

Unit testing focuses verification effort on the smallest unit of software design, the module. The unit testing we have is white box oriented and some modules the steps are conducted in parallel.

1. WHITE BOX TESTING

This type of testing ensures that

· All independent paths have been exercised at least once

· All logical decisions have been exercised on their true and false sides

· All loops are executed at their boundaries and within their operational bounds

· All internal data structures have been exercised to assure their validity.

To follow the concept of white box testing we have tested each form .we have created independently to verify that Data flow is correct, All conditions are exercised to check their validity, All loops are executed on their boundaries.

2. BASIC PATH TESTING

Established technique of flow graph with Cyclomatic complexity was used to derive test cases for all the functions. The main steps in deriving test cases were:

Use the design of the code and draw correspondent flow graph.

Determine the Cyclomatic complexity of resultant flow graph, using formula:

V (G) =E-N+2 or

V (G) =P+1 or

V (G) =Number Of Regions

Where V (G) is Cyclomatic complexity,

E is the number of edges,

N is the number of flow graph nodes,

P is the number of predicate nodes.

Determine the basis of set of linearly independent paths.

3. CONDITIONAL TESTING

In this part of the testing each of the conditions were tested to both true and false aspects. And all the resulting paths were tested. So that each path that may be generate on particular condition is traced to uncover any possible errors.

4. DATA FLOW TESTING

This type of testing selects the path of the program according to the location of definition and use of variables. This kind of testing was used only when some local variable were declared. The definition-use chain method was used in this type of testing. These were particularly useful in nested statements.

5. LOOP TESTING

In this type of testing all the loops are tested to all the limits possible. The following exercise was adospted for all loops:

All the loops were tested at their limits, just above them and just below them.
All the loops were skipped at least once.
For nested loops test the inner most loop first and then work outwards.
For concatenated loops the values of dependent loops were set with the help of connected loop.
Unstructured loops were resolved into nested loops or concatenated loops and tested as above.

Each unit has been separately tested by the development team itself and all the input have been validated.

TEST CASES

1 Test Case 1 – Login

Test 1:

· Incorrect input: An empty requirement field. (user name and password)

· Pass criteria: An appropriate error message should be displayed and the user shouldn’t be allowed to login.

· Correct input: Right user name and password.

· Pass criteria: The user should be directed to the secure web page which the user is requested.

Test 2:

Incorrect input: Wrong user name and/or wrong password.

· Pass criteria: The user shouldn’t be allowed to login to the system and an appropriate error message should be displayed.

· Correct input: Right user name and password.

· Pass criteria: The user should be login to the system and directing to the requested secure web page.

2 Test Case 2 – New User Register

· Incorrect input: An empty requirement field. (first name, last name, middle name, Photo, address, Date Of Birth, phone number, user name, password, e-mail address)

· Pass criteria: An appropriate error message should be displayed and the user shouldn’t be allowed to create an account.

· Correct input: Fill in all requirement fields in correct format.

· Pass criteria: The user information should be added into the database.

Test Case 3 – Generate Report (Customer)

Test 1:

· Incorrect input: An empty requirement field. (Select Date)

· Pass criteria: An appropriate error message should be displayed and the user should not be able to generate a report.

· Correct input: Enter(Select) Correct Date

· Pass criteria: The user (admin) should be allowed to generate the report.

India reaches dizzy Cricket

2012-03-10T04:30:00.002-08:00

India reaches dizzy Cricket

If a select cluster of Indian girls were thrilled on a wonderful Saturday on February 25,1995 at Eden Park. New Zealand, they had good reason for it. To simply say that they won the three nation Centenary series for it. To simply say that they won the three nation Centenary series cup, contesting against world’s top sides like Australia and Newzealand would be a would be a gross understatement: the apt words would be that captain Purinama Rau and her enthusiastic bunch had carved the focus briefly on the national women’s cricket. The sweet success shifted the focus perforce had to organize women’s cricket team, but women’s cricket, perforce has to organize and project itself with maximum intensity to make a greater impact in India.

Indeed, those who were a witness to the National championship which preceded the tour to New Zealand would have only a mediocre description of women’s cricket to narrate. In an age when the popular and well established first class and test cricket despairs from want of support from the public, one cannot expert rosier events for women’s cricket. The overall success in the short series in New Zealand in the Centenary cup final ought to be used as a lever to give a thrust to women’s cricket languish. As it had been until the signal accomplishment by Purnima Raju’s Indians.

Purnima Rau attributed her teams success to the presence of “all rounder’s. Excellent running between the wickets” and to “accurate bowling and sharp fielding” In the end she made a telling comment that “Indian women’s cricket will merit respect now onwards” These that Indian women’s cricket will merit respect now onwards”. These were impromptu comments from a victorious captain, but Purnima also subscribed to a view that the batting seen during the national championship in Bombay was extremely mediocre, with batsman adopting ultra-cautions methods and not making an attempt to play strokes.

Yes. Indian women’s cricket has come a long way since it was incepted some 20 years ago. In this period players like shanta Rangaswamy, Srirupa Bose. Shubanghi Kulkarni. Sudha shah. Diana Eduljee and Sandhya Agrawal managed to make an impact through their Indian vidual efforts. In two decades of time, any sports organizations shold have made a greater impression, but the women’s cricket Association of have made a greater impression, but the Womens cricket India (WCAI) ha not been able to project itself as a cohesive national unit. Obviously there are difference among the state units and within unit. Obviously there are differences among the State units and within the state units. There are other factors which has had a negative effect.

2012-03-10T04:29:00.001-08:00

Various Styles of Teaching in Schools and College

Although there are various styles of teaching in schools and college the telling method continues to be the most popular among them. In college they are usually called “lecture”. This gives a wrong idea that they represent some sort of a one-way-traffic where in the teacher lectures and the pupils listen.

Very often this listening is unfortunately a passive activity. Sometimes the pupils do not listen, but only hear. They may be looking at the teacher but their minds may be elsewhere. Educationists have pointed out many defect in the lecture method in which the involvement of the pupils may be next to nothing. Participatory methods of teaching and learning have their own great merits.

However, owing to reasons of convenience, the lecture method would continue as the prime mode of teaching. The lecture styles has many advantages. It is flexible, helps non-readers, is good for introducing topics can inspire, can easily handle large classes, good for pep talk, can “cover” any amount of content in the available time

On an average a college student may spend 12-20 hours a week listening to lectures. This shown that you should necessarily improve your efficiency of to listening. By listening to lectures effectively, your study at home will be simlistening. By listening to lecture, you should try to summaries the content of the lecture mentally. There may be unexpected road-blocks in our thought process. Questions would emerge in our mond. Finding proper thought process. Questions, either through self-study from books or through clarfications from the teacher will confirm our learning.

Before we go into nuts and bolts of classroom listening, let us look at listening in general. As Oliver Wendell Holmes once said, it is the province of knowledge to speak and it is the privilege of wisdom to listen. Unfortunately knowledge to speak and it is the privilege of wisdom to listeners; we try to project our image before others by speaking more and listening less

A good listeners is usually popular everywhere. He learns many things from others.