In this blog, we’ll look at eight Redis metrics you might not be tracking. We’ll explain why they matter and how watching them can help you avoid performance problems and unexpected crashes.

1. Memory use efficiency (the fragmentation ratio) 

Redis uses ways to store data in memory that can sometimes lead to wasted space, even if it looks like you have enough memory. The mem_fragmentation_ratio field shows how well Redis is using the memory it has.

Why it matters

• A high ratio (over 1.5) means that memory is being wasted because it’s not organized well.

• This can cause Redis to use more memory and cost you more in the cloud.

How to fix it

• Change the maxmemory-policy setting to help Redis manage memory better.

• Adjust the settings for jemalloc, the tool Redis uses to handle memory.

2. The cache hit rate (keyspace hits vs. keyspace misses) 

The Redis hit rate is how often Redis finds the data it’s asked for. This rate calculated by dividing the number of times Redis found the data (keyspace_hits) by the total number of times it was asked (keyspace_hits + keyspace_misses). A low rate means Redis isn’t doing a good job of caching.

Why it matters

• A low hit rate (under 80%) means Redis often can’t find the data, so it has to go to the main database more, which slows things down.

How to fix it

• Change how long data stays in the cache (time to live (TTL) values).

• Make the cache bigger or use a system (like the least recently used policy) that keeps the most used data.

3. Removed and expired keys 

Redis removes keys when it runs out of memory, and keys also disappear when their time limit (TTL) is up. Watching evicted_keys (removed due to memory) and expired_keys (removed due to time) helps you know if Redis is working as expected.

Why it matters

• If there are lots of evicted keys, it might mean your Redis system doesn’t have enough memory.

• A sudden jump in expired keys could mean you’re losing data you didn’t expect to.

How to fix it

• Change Redis’ memory rules and how it decides what to remove.

• Make sure the time limits you set for keys match what your application needs.

4. Client buffer and waiting commands 

Redis keeps a buffer for each client to handle network traffic. The client_output_buffer_limit metric shows how much data is waiting to be sent to clients.

Why it matters

• A large client buffer can mean there’s a problem with the network or that clients are slow at processing data.

• Commands waiting in line can delay important requests.

How to fix it

• Check your network setup and increase the bandwidth if needed.

• Add more Redis systems to spread the work.

5. Replication lag 

If you have Redis set up with a main system (primary) and copies (replicas), replication lag is the delay between them.

Why it matters

• A big lag (over one second) means the copies might have old data.

• This can cause problems with data consistency if your main system fails.

How to fix it

• Check the network connection between the main and copy systems.

• Watch and adjust how often the copies sync with the main system.

6. Slow log entries 

Redis has a feature that logs commands that take longer than a certain amount of time. Watching these slow log entries helps you find commands that are slowing things down.

Why it matters

• If there are lots of slow commands, it means Redis isn’t running efficiently.

How to fix it

• Find and improve the slow commands (for example, don’t use KEYS on large amounts of data).

• Use SCAN instead of KEYS for large datasets.

7. Rejected connections   

Redis will refuse new connections when it reaches its limit (maxclients). Watching rejected_connections helps you see if you’re hitting this limit before it causes problems.

Why it matters

• Rejected connections mean new parts of your application can’t talk to Redis.

How to fix it

• Increase the maxclients setting or spread connections across multiple Redis systems.

• Use connection pooling in your application to reuse connections.

8. AOF rewrite activity

If Redis uses a system, like the append-only file (AOF), to save data to the disk, it sometimes rewrites this file. Watching aof_rewrite_in_progress and can help you avoid disk issues.

Why it matters

• Frequent rewrites can put a lot of stress on your disk, slowing down Redis.

How to fix it

• Change the AOF rewrite rules to avoid writing to the disk too often.

• Adjust Redis’ save settings based on how important your data is.

Monitor Redis the right way 

To really know how well Redis is performing, you need to look beyond the basic numbers. By tracking these metrics, you can catch problems early, use your resources better, and make sure your Redis system is stable and fast.

Using a good monitoring tool like ManageEngine Applications Manager can give you the deep insights you need to understand your Redis system’s health, find issues quickly, and keep your applications running smoothly for your users.

Curious to explore Redis monitoring capabilities in more depth? Check out these helpful resources:

• Redis monitoring in Applications Manager

• Redis monitoring 101: Metrics to watch

• Redis monitoring in Applications Manager: A deep dive

• What is Redis monitoring?

Want to see how Applications Manager can help you monitor Redis effectively? Download a free, 30-day trial now!

The post Monitoring Redis beyond the basics: Hidden metrics that matter appeared first on ManageEngine Blog.

Why is SLA management crucial for enterprises?

Managing SLAs helps IT teams ensure their services meet business expectations. It helps admins to track every issue and potential breach, reduce the risk of oversight, and enforce accountability. With proper oversight, admins will be able to ensure that the promised baselines in the SLAs are met.

What happens if SLAs are not managed efficiently?

Loss of trust

When organizations fail to meet agreed performance baselines, it leads to dissatisfaction among end users and tarnishes trust. Organizations that establish SLAs are expected to deliver seamless and reliable service. Violating such agreements can cause mistrust among service providers and recipients.

Loss of revenue

Most SLAs prescribe financial compensation for non-compliant SLAs as penalties. Repeated violations can cause significant financial loss. Also, with prolonged response time and issue fixes, the user experience is bound to be affected. In such cases, organizations are prone to losing brand image and revenue due to delayed business operations and underperforming services.

Operational issues

Poor SLA management can create inefficiencies within the organization, such as overlooked or misdirected escalations or reactive management instead of proactive planning. This disrupts workflows and reduces overall productivity.

Scalability issues

SLAs that are not reviewed or updated regularly fail to align with dynamic business objectives. This occurs when KPI trends look good periodically but fail to deliver quality service to end users.

Real-world examples of SLA failures

You might say, “What could possibly go wrong if an SLA is violated?” Give the following cases a skim:

Slack’s global outage (January 2021)

One of the most prominent communication media among the corporate bodies, Slack, experienced a global outage on Jan. 4, 2021. It disrupted communication among millions of users who were unable to leverage the service for almost five hours. The root cause turned out to be a server scaling issue, which led to service downtime, and an unnoticed SLA violation, affecting the availability of the service globally. This resulted in significant financial and reputational loss. Learn more.

According to Gartner, over 70% of IT service failures stem from mismanaged SLAs or communication breakdowns.

Barclays Bank outage (January 2025)

UK-based multinational banking firm, Barclays, faced a similar but more severe situation than Slack. In January 2025, the banking organization suffered a critical outage that took three whole days to be resolved. This resulted in a major failure in fund transactions at an error rate of over 50%. The outage broke the payment gateways right on the deadline to file self-assessment tax returns, making public outcry worse. Barclays confirmed that it is going to pay over $6.6 million for compensating “distress” due to the service crashes.

Websites, application servers, online banking, and telebanking services were affected by this outage. Users were logged out of their sessions, payments didn’t go through, and funds froze midway overnight. The prolonged mean time to resolve highlighted the need for proactive management instead of reactive approaches. Although Vim Maru, CEO of Barclays UK, stated it was just a software problem, the internet speculated that it could be anything from a mainframe OS configuration issue to a faulty deployment. Given the array of services provided by Barclays and the infrastructure, it could be a broken interdependency or an overlooked escalation. This incident points out how violated SLAs and blind spots in IT infrastructure can cost organizations dearly. Learn more.

Here’s how you can ensure strict SLA compliance with Applications Manager

ManageEngine Applications Manager can be used to manage SLAs by defining service targets, monitoring performance, and reporting on SLA violations.

Define KPIs and establish baselines

Define service-centric KPIs that let you understand the quality of service delivery. Configure thresholds for each metric to alert you of violations at multiple severity levels. This helps you understand overall service performance and highlights anomalies that potentially affect service reliability.

Monitor performance metrics in real time

With Applications Manager’s real-time performance polling, you can monitor your KPI trends in real time. Understanding service performance on the go helps you ensure the uptime and availability of your service.

Proactively monitor SLA performance

Adopt proactive monitoring techniques to monitor your SLAs effectively. Stay ahead of critical violations with Applications Manager’s response automation and performance forecasts. Configure automated responsive actions and alert escalations to resolve anomalies before they lead to critical breaches.

Ensure seamless escalations

When a monitored metric falls outside of the defined SLA parameters, Applications Manager can trigger alerts to notify relevant personnel. Configure email, SMS, and Slack messages to trigger associated teams on under-delivering SLAs and collaborate on solutions to prevent service downtime. You can also integrate Applications Manager with ManageEngine ServiceDesk Plus to streamline the end-to-end service management process. This integration allows for automated ticket creation and escalation when SLAs are violated, further enhancing the efficiency of SLA management.

Scale SLAs efficiently

Update SLAs from time to time to ensure they scale up to customer needs and stay aligned with dynamic business requirements. This eliminates the scope for under-delivery of services and enhances service productivity.

Quickstart SLA management with Applications Manager now!

You can get started on SLA management with Applications Manager in minutes! Try out the tool by downloading and installing our 30-day trial version. Applications Manager offers monitoring support for over 150 technologies like web services, cloud services, middleware, VMs, ERPs, databases, containers, web servers, applications servers, cloud applications, and many more. You will be able to configure SLAs for each service and track uptime on the go.

Need help? Schedule a demo with our experts now!

The post Why SLA management is crucial for enterprises and the risks of failing to manage SLAs properly appeared first on ManageEngine Blog.

From interactive Zoho Charts that simplify trend analysis to a revamped UI for effortless navigation, these upgrades will empower you to analyze data efficiently, identify performance bottlenecks instantly, and help you make data-driven decisions with confidence.

What’s changed in the new Applications Manager’s dashboard?   

In our latest update, every enhancement is designed to streamline IT monitoring and optimize workflows:

1. Real-time interactive data visualization with Zoho Charts   

The transition to Zoho Charts introduces an interactive way to visualize your data, making it easier to pinpoint performance issues.

• Instant tooltips provide immediate context when hovering over data points, allowing teams to quickly diagnose spikes in server latency without manually searching logs.

• Zoom and pan features allow you to focus on specific timeframes, making it easier to identify recurring slowdowns and spot patterns in resource utilization.

• Dynamic animations enhance data clarity, helping you instantly recognize trends in application uptime and server performance.

By zooming into performance dips, panning across historical data, and leveraging tooltips, you can detect anomalies faster and accelerate root cause analysis—all within an intuitive and highly responsive dashboard.

2. Advanced charting features for granular data analysis   

Performance trends become clearer with enhanced line, bar, and dial charts that offer better readability and deeper insights into your IT infrastructure.

• Zoomable line charts allow teams to track critical metrics over time, such as CPU spikes during peak traffic hours, ensuring bottlenecks are identified before they escalate into major issues.

• Scrollable legends make it easy to manage multiple metrics simultaneously so you can focus on the most relevant data without the clutter.

• Overview charts provide a summarized snapshot, perfect for tracking long-term resource utilization trends and setting custom timeframes for performance comparisons.

With these enhancements, seamless trend analysis helps IT teams pinpoint irregularities, optimize resources, and streamline reporting.

3. Revamped Applications Manager’s dashboard UI for effortless navigation   

A well-structured dashboard eliminates friction, allowing teams to detect and resolve issues faster. The redesigned Applications Manager dashboard improves usability with:

• A cleaner layout that places key metrics front and center, helping IT admins instantly spot and address critical performance deviations without unnecessary distractions.

• Seamless consistency with the APM Insight Dashboard, ensuring a unified monitoring experience across different platforms for comprehensive visibility.

• Dark and light modes, optimized for different working conditions, reduce eye strain and enhance visibility in Network Operations Centers (NOCs) and 24/7 monitoring.

With an optimized layout and improved navigation, you can access critical data faster, making it easier to detect anomalies and enhance operational efficiency.

4. SVG-based severity status icons for faster issue recognition   

Clear, visually distinct alerts are crucial in high-pressure IT environments where every second matters. The introduction of SVG-based severity status icons ensures that you can immediately recognize the status of your system health.

• Sharper, scalable icons eliminate confusion when distinguishing between healthy, warning, and critical states, making outage detection faster and more effective.

• Optimized for both dark and light modes, these icons maintain high contrast, ensuring teams can instantly identify system status in any viewing condition.

• Faster issue prioritization enables you to immediately respond to high-impact system failures, reducing downtime and preventing minor issues from escalating.

By enhancing alert visibility and reducing response time, the dashboard upgrades ensure faster incident resolution and a more proactive IT monitoring approach.

How these enhancements will transform IT monitoring   

The updated Applications Manager’s dashboard brings smarter, more efficient IT monitoring enhancements. By integrating interactive charts, deeper data analysis tools, and a more user-friendly UI, you can:

• Resolve performance issues faster with real-time visual insights.

• Navigate dashboards effortlessly, focusing on critical metrics without distractions.

• Make data-driven decisions efficiently, leading to better system reliability and uptime.

• Streamline reporting, making performance insights more accessible for stakeholders.

Upgrade to the enhanced Applications Manager’s dashboard today     

These improvements are available starting from version 174700, offering both existing users and new adopters a more efficient, insight-driven monitoring solution. If you haven’t yet explored Applications Manager, now is the perfect time to start with a free, 30-day trial and experience these enhancements firsthand.

The future of IT monitoring starts here  

Modern IT environments demand intelligent, data-driven monitoring solutions. With these powerful dashboard enhancements, Applications Manager provides you with the tools you need to stay proactive, optimize performance, and respond to issues with greater speed and accuracy.

Start exploring these features today to improve your IT monitoring experience. Your feedback continues to drive innovation, ensuring Applications Manager evolves with your needs—now and in the future. Take control of your monitoring journey with Applications Manager!

The post Applications Manager’s dashboard: What’s new? appeared first on ManageEngine Blog.

In this blog, we will explore the importance of cloud migration monitoring and how it can help businesses ensure a smooth and successful transition to the cloud.

What is cloud migration monitoring? 

Cloud migration monitoring is a crucial aspect of any successful cloud migration strategy. It involves the continuous tracking and analysis of data and applications during the migration process to ensure a smooth and efficient transition to the cloud. This process involves monitoring various metrics, such as performance, availability, and security, to identify any potential issues and address them proactively. With the increasing complexity of cloud environments, cloud monitoring during migration becomes even more critical, helping organizations stay on top of their cloud infrastructure and make informed decisions. It also enables them to optimize their resources, reduce costs, and ensure a seamless user experience. By closely monitoring the migration process, organizations can mitigate risks and ensure a successful migration to the cloud, ultimately driving business growth and innovation.

Why is cloud migration monitoring important?

Cloud migration is a crucial step for any organization—it involves transitioning data and applications from on-premises servers to a cloud-based environment, which offers numerous benefits such as cost savings, scalability, and increased flexibility. However, this transition can be risky, time- and resource-intensive, and can lead to unforeseen challenges and disruptions if not administered with care.

Effective monitoring helps organizations:

• Track migration progress and resolve potential issues promptly.

• Minimize downtime and ensure business continuity.

• Maintain security and compliance by monitoring access, detecting unauthorized activities, and adhering to industry regulations.

• Control costs by tracking resource usage and avoiding unnecessary expenditures.

A well-structured cloud migration monitoring strategy ensures a secure, smooth, and efficient transition; for this, having a robust cloud migration monitoring solution in place is crucial.

Challenges in cloud migration

While cloud migration can be a huge leap in enhancing overall infrastructure performance in the long run, the challenges can be heavy and painful compared to the benefits. Following are some of the key challenges experienced in cloud migration and some ways monitoring can help:

• Complex and diverse environments: Cloud migration involves moving applications and data from on-premises environments to the cloud, which can be complex and diverse. This makes it challenging to monitor and manage the migration process, as different applications and data may have different requirements and dependencies.

• Lack of visibility: One of the biggest challenges in cloud migration is the lack of visibility into the entire migration process. This can make it difficult for organizations to track the progress of their migration, identify any potential issues, and make necessary adjustments.

• Performance issues: During the migration process, there is a risk of performance issues, such as slow response times and downtime. These issues can have a significant impact on business operations and the user experience. Monitoring performance metrics, such as response times, network latency, and resource utilization, is crucial to identify and address any performance issues.

• Cost management: Cloud migration can be expensive, and organizations need to closely monitor their costs to ensure they stay within budget. This can be challenging as cloud costs can vary based on usage and demand. Organizations need to have a clear understanding of their cloud usage and continuously monitor and optimize costs to avoid overspending.

• Integration with existing systems: Many organizations have a mix of on-premises and cloud-based systems, and migrating to the cloud requires integration with existing systems. Any issues with integration can impact the overall migration process and business operations, so monitoring integrations is crucial.

• Limited control: When migrating to the cloud, organizations are essentially handing over control of their infrastructure to the cloud service provider. This can make it challenging to manage the migration process, as organizations may not have the same level of control and visibility as they do with on-premises systems.

• Continuous monitoring: Cloud migration is an ongoing process, and organizations need to continuously monitor their cloud environments to ensure they are operating efficiently and effectively. This requires a robust and scalable monitoring strategy to keep track of changes and identify any potential issues in real-time.

Key aspects to monitor in cloud migration life cycle

Cloud migration is a complex process that requires careful planning and execution to ensure a smooth transition. To help organizations navigate this process, there are typically four phases of cloud migration that need to be followed.

 1. Assessment phase: 

The first phase of cloud migration is the assessment phase. This is where businesses evaluate their current IT infrastructure and determine which applications and workloads are suitable for migration to the cloud. It is important to conduct a thorough assessment to identify any potential challenges or risks that may arise during the migration process. This phase also involves setting goals and objectives for the migration, such as cost reduction, scalability, and improved performance.

What needs to be monitored:

• Current IT infrastructure: This includes servers, storage, networks, and applications. It is important to have a clear understanding of the existing infrastructure to determine which components can be migrated to the cloud.

• Application dependencies: Applications often have dependencies on other applications or systems. It is crucial to identify these dependencies to ensure a smooth migration process.

• Performance metrics: Monitoring performance metrics such as CPU usage, memory usage, and network traffic can help identify any potential bottlenecks or issues that may arise during the migration process.

 2. Planning phase: 

The second phase of cloud migration is the planning phase. This is where businesses develop a detailed plan for the migration, including timelines, resource allocation, and potential risks. The plan should also include a backup and disaster recovery strategy to ensure data is protected during the migration process. It is important to involve all stakeholders in the planning phase to ensure a smooth and successful migration.

What needs to be monitored:

• Timeline: It is important to monitor the timeline to ensure that the migration is on track and any delays are addressed promptly.

• Resource allocation: Monitoring resource allocation, such as server and storage capacity, can help identify any potential issues that may arise during the migration process.

• Backup and disaster recovery strategy: It is crucial to regularly monitor the backup and disaster recovery strategy to ensure that data is protected throughout the migration process.

 3. Migration phase: 

The third phase of cloud migration is the actual migration phase. This is where the data, applications, and workloads are moved to the cloud. The migration process can be done in several ways, including a lift-and-shift approach, where applications are moved as-is to the cloud, or a re-architecting approach, where applications are optimized for the cloud environment. It is important to have a detailed plan and test the migration process before executing it.

What needs to be monitored:

• Data transfer: Monitoring the transfer of data to the cloud is crucial to ensure that all data is successfully migrated.

• Application performance monitoring: It is important to monitor the functionality of applications during and after the migration process to ensure they are working as expected.

• User experience: Monitoring the user experience can help identify any performance issues that may arise during the migration process.

 4. Optimization phase: 

The final phase of cloud migration is the optimization phase. This is where businesses fine-tune their cloud environment to ensure optimal performance and cost-efficiency. This phase involves monitoring and analyzing data to identify areas for improvement and implementing changes to optimize the cloud environment.

What needs to be monitored:

• Cost: Monitoring cloud costs is crucial to ensure that the organization is not overspending on resources.

• Performance: Regularly monitoring performance metrics can help identify any issues that may impact the performance of applications and workloads.

• Scalability: Monitoring the scalability of the cloud environment can help ensure that it can handle the organization’s growing needs.

By following these four phases and monitoring key aspects at each stage, businesses can ensure a successful and smooth transition to the cloud. It is also important to regularly review and update the cloud environment to ensure it continues to meet the organization’s needs.

Best practices in cloud migration monitoring 

Cloud migration monitoring is a critical process that helps organizations ensure that their cloud migration is successful and efficient. Here are some best practices to follow for effective cloud migration monitoring:

1. Define clear objectives and goals: Before starting the cloud migration process, it is important to define clear objectives and goals. This will help you determine what needs to be monitored and how to measure the success of your migration.

2. Establish a baseline: Establishing a baseline is crucial for monitoring the performance of your cloud migration. This will help you compare the performance of your applications before and after the migration and identify any potential issues.

3. Monitor all components: It is important to monitor all components of your cloud environment, including applications, servers, databases, and network infrastructure. This will help you identify any bottlenecks or performance issues that may arise during the migration process.

4. Monitor security and compliance: As you migrate your data and applications to the cloud, it is important to ensure that they remain secure and compliant with industry regulations. Monitor security and compliance metrics to identify any potential vulnerabilities.

5. Monitor cloud expenses: Cloud migration can bring cost savings, but it is important to monitor your cloud costs to ensure that you are not overspending. Use cost monitoring tools such as ManageEngine CloudSpend to track your usage and identify any areas where you can optimize costs.

6. Monitor the user experience: The ultimate goal of cloud migration is to improve the user experience. Monitor user experience metrics, such as response time and page load time, to ensure that your applications are performing well in the cloud.

7. Establish a monitoring schedule: Set up a regular monitoring schedule to track the performance of your cloud environment. This will help you identify any issues early on and take corrective actions.

8. Analyze and act on data: Monitoring is only effective if you analyze the data and take action. Use the insights from your monitoring to identify areas for improvement and take proactive measures to optimize your cloud environment.

9. Continuously monitor post-migration: Cloud migration is an ongoing process, and it is important to continue monitoring your environment even after the migration is complete. This will help you identify any issues that may arise and make necessary adjustments.

Devise your cloud migration monitoring strategy with Applications Manager!   

Start tracking and managing your cloud migration into your cloud infrastructure with Applications Manager. Don’t let the complexities of cloud migration slow you down; gain real-time visibility into the performance of your cloud applications and effectively monitor your cloud environment. In addition to robust cloud monitoring, you can also access detailed insights on application performance, infrastructure health, and the digital experience. Quickly detect and resolve issues, automate tasks, and ensure uninterrupted user experiences. Schedule a personalized demo or try our 30-day, free trial to fully experience the capabilities of our cloud migration monitoring solution.

The post Key factors to consider during cloud migration monitoring appeared first on ManageEngine Blog.

Blog ini membahas manfaat pemantauan cloud yang komprehensif serta insight penting yang Anda butuhkan untuk memaksimalkan performa layanan cloud Anda.

Mengapa Anda perlu tool monitoring cloud?

Jika Anda menggunakan layanan public cloud seperti Amazon Web Services (AWS) atau Azure, kemungkinen besar Anda telah menggunakan layanan monitoring yang ditawarkan oleh vendor public cloud, sebab kedua layanan ini terintegrasi. Namun, pada infrastruktur organisasi berskala besar, visibilitas yang lebih luas dari sekadar parameter dasar yang ditawarkan oleh layanan ini menjadi sangat penting untuk monitoring berkelanjutan. Misalnya, jika Anda memiliki infrastruktur hybrid cloud atau aplikasi custom internal, kemungkinan besar proses monitoring Anda akan lebih kompleks dengan penggunaan beberapa tool untuk teknologi yang berbeda.

Tool monitoring cloud yang dapat memantau server fisik dan virtual, bersamaan dengan layanan cloud sangat dibutuhkan. Sebab, tool ini dapat membantu Anda mengamankan seluruh ekosistem aplikasi IT dari ancaman kinerja yang tidak terdeteksi. Selain itu, tool juga dapat memprediksi penggunaan cloud dan merencanakan kapasitas cloud di masa depan.

Beberapa parameter penting pada layanan monitoring cloud populer

1. Compute: Memory, disk, dan jaringan

Baik Anda menggunakan AWS, Azure, Oracle Cloud Infrastructure (OCI), atau Google Cloud Platform (GCP), Compute adalah salah satu layanan cloud pertama. Tak diragukan lagi, aspek utama dalam monitoring kinerja cloud adalah melacak parameter kinerja yang penting, seperti penggunaan memori, ruang disk, dan traffic jaringan. Memantau metrik ini sangat penting untuk memastikan ketersediaan sumber daya dan kinerja aplikasi yang lancar. Analisis historis dari tren penggunaan ini juga dapat membantu dalam perencanaan kapasitas di masa depan.

2. Penyimpanan object: Jumlah object, ukuran object, permintaan/menit, dan traffic jaringan

Tergantung pada vendor, metrik block storage dapat bervariasi, tetapi umumnya menggunakan parameter yang serupa. Sementara, jumlah object, ukuran object, permintaan per menit, detail jaringan, dan latensi adalah metrik penting untuk pemantauan cloud di AWS, GCP, dan OCI, Azure menggunakan ukuran blob dan permintaan blob sebagai gantinya.

Metrik ini membantu memahami beban pada layanan storage serta mengidentifikasi error dalam pemrosesan permintaan dengan menemukan area yang bermasalah.

3. Relational database: Penggunaan CPU, storage space, koneksi database, dan latensi jaringan

Relational database populer di kalangan developer dan digunakan dengan masif oleh banyak perusahaan besar. Seperti halnya infrastruktur fisik, database cloud juga memerlukan pemantauan, terutama pada metrik seperti penggunaan CPU, sesi database, detail tablespace, dan koneksi database. Pemantauan mendalam dengan ambang batas yang tepat sangat penting untuk mencegah masalah yang dapat mengganggu kinerja, seperti lambatnya waktu respons aplikasi akibat meningkatnya jumlah koneksi pengguna atau eksekusi kueri yang lambat.

Anda dapat mempelajari lebih jauh tentang berbagai parameter penting yang perlu dilacak untuk menerapkan monitoring cloud yang komprehensif melalui beberapa layanan dari link berikut:

• Monitoring AWS

• Monitoring Azure

• Monitoring GCP

• Monitoring OCI

• Monitoring OpenStack

• Monitoring Office 365

Menerapkan strategi monitoring cloud yang tepat

Memantau komponen IT mana pun, baik itu server fisik, layanan cloud, atau aplikasi end-user, semuanya membutuhkan visibilitas mendalam. Dengan visibilitas, Anda bisa menemukan banyak insight yang mengidentifikasi adanya masalah kinerja. Sering kali, masalah ini tidak terdeteksi dan baru akan muncul melalui masalah kinerja atau downtime bisnis.

Memantau elemen yang penting

Hal pertama yang harus Anda lakukan adalah memastikan Anda sudah memantau semua elemen penting dalam ekosistem IT Anda. Contohnya seperti kinerja infrastruktur server (fisik,  virtual, atau cloud), jaringan, dan aplikasi. Hal ini bisa dicapai dengan menerapkan solusi monitoring komprehensif yang mampu menyediakan insight mendalam tentang masalah kinerja dan memfasilitasi capacity planning melalui analisis riwayat tren.

Tak peduli vendor cloud manapun yang Anda gunakan, tanpa visibilitas mendalam terhadap parameter penting, masalah kinerja tidak akan terdeteksi. Pada akhirnya, ini menyebabkan service delivery bisnis Anda berada dalam ancaman besar.

Memantau infrastruktur cloud and on-premise dari satu dashboard

Seiring dengan peralihan bertahap organisasi modern menuju ekosistem cloud-only atau hybrid cloud, penting untuk memastikan bahwa tool monitoring cloud Anda menyediakan satu dasbor terpadu untuk mengumpulkan dan menghubungkan data dari elemen fisik, virtual, dan cloud.

Fleksibilitas ini tidak hanya mempermudah transisi tetapi juga mendukung aplikasi lama serta server fisik, sehingga mencakup seluruh ekosistem IT Anda dan menjadikan tool monitoring cloud sebagai investasi yang berharga.

Mengautomasi corrective task rutin

Mengotomatiskan tugas korektif rutin memungkinkan Anda lebih fokus pada situasi yang memerlukan intervensi manusia. Otomasi merupakan bagian penting dari proses DevOps dan dapat meningkatkan produktivitas di lingkungan cloud. Misalnya, Anda dapat mengatur tindakan otomatis seperti memulai, menghentikan, atau me-restart instance cloud ketika ambang batas terlampaui. Hal ini membantu Anda melakukan scaling secara otomatis atau menonaktifkan sumber daya yang tidak terpakai, sehingga mengelola biaya cloud lebih efisien, terutama jika Anda menggunakan model harga pay-as-you-go.

Mengelola biaya

Penggunaan layanan cloud berkelanjutan dapat meningkatkan biaya. Tentu saja, Anda memiliki budget bulanan untuk pengeluaran cloud Anda. Namun, tool monitoring kinerja cloud harus bisa memantau tagihan cloud dan memberi notifikasi jika ada ambang batas tertentu yang terlampaui. Dengan demikian, Anda akan selalu tahu tentang penggunaan dan biaya cloud, memungkinkan Anda untuk merancang budget cloud yang tepat.

ManageEngine Applications Manager menawarkan monitoring hybrid cloud yang mendalam untuk server fisik maupun virtual, untuk layanan cloud populer seperti AWS, Azure, GCP, OCI, dan OpenStack, untuk layanan SaaS seperti Office 365 dan lebih dari 130 aplikasi bisnis lainnya. Monitoring komprehensif ini memungkinkan Anda mendapatkan visibilitas penuh terhadap ekosistem aplikasi IT Anda. Sehingga, Anda dapat mengonfigurasi alert dan tindakan remediasi secara otomatis, memahami kinerja dan ketersediaan secara keseluruhan, dan merancang kapasitas cloud yang tepat untuk menyajikan layanan terbaik bagi pelanggan.

Jika Anda baru mengenal Applications Manager, hubungi kami melalui eval-apm@manageengine.com atau uji coba gratis 30 hari untuk mempelajari lebih lanjut!

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Challenge #1: The transient nature of containers (ephemerality) 

Containers are inherently ephemeral, with their life cycles often measured in seconds. This dynamism creates observability gaps for monitoring tools designed for static infrastructures.

Practical solutions:

• • Implement real-time monitoring solutions that provide immediate insights into container health and performance to prevent blind spots.

  • Leverage Kubernetes-native observability tools like Prometheus or comprehensive container monitoring tools like ManageEngine Applications Manager for effective container life cycle tracking.

  • Utilize distributed tracing (OpenTelemetry, Applications Manager) to maintain visibility across short-lived container instances.

Why it matters: Without real-time monitoring, troubleshooting ephemeral containers becomes impossible. This can lead to undetected failures and degraded performance.

Challenge #2: Monitoring at scale 

Large-scale deployments, involving hundreds or thousands of containers across clusters, can overwhelm conventional monitoring systems, resulting in alert fatigue and inefficient troubleshooting.

Practical solutions:

• • Employ auto-discovery mechanisms to detect new container instances without manual configuration dynamically.

  • Utilize AI-driven anomaly detection to minimize false positives and reduce alert noise.

  • Optimize log aggregation and centralization for efficient data querying and analysis.

Why it matters: Without automated scalability, IT teams often struggle to track performance bottlenecks. This can cause slow response times and inefficient resource utilization.

Challenge #3: Managing log and metric overload 

The sheer volume of logs and metrics generated by containers can lead to significant storage and processing overhead.

Practical solutions:

• • Implement log sampling and retention policies to prioritize relevant data.

  • Utilize log aggregation platforms (e.g., ELK Stack, Fluentd, Loki) to streamline log management.

  • Consider edge-based monitoring solutions to process data locally and reduce cloud ingestion costs.

Why it matters: Without proper log management, IT teams can end up wasting time sifting through log files. This makes it harder to detect critical performance issues.

Challenge #4: Achieving observability in multi-cloud and hybrid environments 

Unified monitoring across on-prem, hybrid, and multi-cloud deployments presents a significant challenge due to the lack of standardization and interoperability.

Practical solutions:

• • Utilize cloud-agnostic monitoring tools like ManageEngine Applications Manager to collect and analyze data across environments.

  • Ensure consistent logging and tracing frameworks across all containerized workloads.

  • Implement service meshes (Istio or Linkerd) to enhance inter-service communication visibility.

Why it matters: A lack of unified monitoring creates blind spots, making it difficult to correlate issues and optimize performance across multiple environments.

Challenge #5: Addressing security and compliance risks 

Containers introduce unique security vulnerabilities and require rigorous monitoring to maintain compliance with industry standards.

Practical solutions:

• • Deploy runtime security monitoring tools (e.g., Falco, Sysdig Secure) to detect real-time anomalies.

  • Implement container image scanning (e.g., Trivy, Clair) to identify vulnerabilities before deployment.

  • Enforce role-based access control (RBAC) and least privilege policies to mitigate unauthorized access.

Why it matters: Weak security measures can expose containers to attacks, leading to data breaches and non-compliance with regulatory standards.  

While container monitoring presents unique challenges, a strategic approach with the right tools can achieve comprehensive observability, improved performance, and enhanced security. By embracing real-time monitoring, automation, and intelligent analytics, teams can proactively address issues and ensure a seamless user experience. It’s about refining our monitoring stack to align with the dynamic nature of cloud-native environments.

Container monitoring with ManageEngine Applications Manager  

ManageEngine Applications Manager offers a robust solution for organizations seeking to gain comprehensive visibility into their containerized environments and plays a significant role in helping them effectively monitor their containerized environments. Here’s a breakdown of its capabilities in this area:

Key capabilities of Applications Manager

• Comprehensive container monitoring:

• • Applications Manager provides in-depth monitoring for popular container technologies like Docker, OpenShift, and Kubernetes.

  • It offers visibility into the health and performance of containers, ensuring that applications running within them are operating optimally.

• Key performance indicator (KPI) tracking:

• • The tool enables users to track essential container KPIs, including resource utilization (CPU, memory, disk, network), response times, and error rates.

  • This data helps identify performance bottlenecks and potential issues before they impact end-users.

• Auto-discovery:

  • Automatically discovers new containers and services, ensuring full coverage of your dynamic infrastructure, regardless of where they spin up

• Kubernetes monitoring:

• • Given the complexity of Kubernetes orchestration, Applications Manager offers specialized monitoring capabilities for Kubernetes clusters.

  • It allows administrators to monitor various Kubernetes components, such as pods, nodes, and services, to provide a holistic view of the cluster’s health.

• Docker monitoring:

• • Applications Manager provides granular visibility into the performance of individual Docker containers. This is crucial for understanding the resource consumption and behavior of each containerized application.

  • It monitors key metrics like CPU usage, memory utilization, network I/O, and disk I/O at the container level.

• OpenShift Monitoring:

• • Applications Manager monitors the overall health of OpenShift clusters, providing insights into the performance of nodes, pods, and services.

  • It helps identify potential issues that could impact the availability and performance of applications running on OpenShift.

• Real-time insights and alerting:

• • Applications Manager provides real-time insights into container performance, enabling teams to quickly identify and address issues.

  • It also offers robust alerting capabilities, notifying administrators of critical events and performance deviations.

  • It also helps reduce manual intervention by automating container actions in case of threshold violations.

• Troubleshooting and root cause analysis:

• • The tool provides detailed diagnostics and root cause analysis capabilities, helping teams pinpoint the source of performance problems.

  • This speeds up troubleshooting and reduces mean time to resolution (MTTR).

  • ML-based anomaly detection helps recognize performance degradation before end users are affected.

• Advanced analytics:

• • Applications Manager helps understand historical performance and performance trends by collecting and aggregating data over time.

  • Forecast reports help anticipate the resource consumption and growth that organizations can use to plan capacity and right-size their resources.

• Integration with Istio service mesh

  • Monitor every aspect of your Istio service mesh environment, including latency, traffic routing, resource usage and service health.

With these capabilities, Applications Manager empowers organizations to maintain optimal performance and availability in their container environments. Take a first-hand experience by downloading a 30-day free trial, or schedule a demo for a guided experience.

The post Navigating container monitoring: Key challenges and practical solutions appeared first on ManageEngine Blog.

1. Latency: Enhancing response efficiency 

Latency, the time taken to process a request from initiation to completion, directly impacts the user experience. High latency can cause sluggish application responses, leading to user frustration and potential revenue loss. It’s crucial to monitor:

• Average response time:

• • This metric provides a baseline understanding of the typical duration required for request fulfillment, offering a general overview of system responsiveness. A consistently low response time ensures a smooth user experience and enhances engagement.

• 95th and 99th percentile response times:

• • These percentiles are essential for identifying instances of extreme latency, which can significantly degrade user satisfaction. They pinpoint potential bottlenecks that may not be apparent in average response time metrics. These outliers tell you where you need to focus your optimization efforts to prevent slow performance during peak usage.

• Dependency latency:

• • This metric measures the time expended during interactions between microservices and external resources, such as databases or third-party services. It highlights potential integration-related performance issues.

By keeping latency low, you can ensure a faster, smoother user experience, leading to better retention and user satisfaction.

2. Throughput: Measuring system capacity 

Throughput, the number of requests a microservice handles per unit time, assesses system capacity. If throughput is low despite high resource availability, it may denote inefficient request handling, leading to performance issues. You must examine:

• Requests per second (RPS):

• • This metric quantifies the service’s capability to process incoming requests within a given second, providing a direct measure of its handling capacity. A sudden drop in RPS could mean system strain or service degradation, and needs immediate attention.

• Transactions per second (TPS):

• • Particularly relevant in database-intensive and high-load scenarios, this metric evaluates the system’s ability to execute transactions within a second, revealing its performance under pressure. Monitoring TPS helps you understand database efficiency and ensures smooth transaction processing.

A high throughput indicates efficient request processing and scalability, allowing services to handle increasing user loads seamlessly.

3. Error rates: Identifying system anomalies 

Error rates, i.e., the percentage of failed requests, are critical for detecting bugs, network failures, or configuration errors. A rising error rate can signal application instability, leading to service disruptions. You need to pay attention to:

• HTTP error codes (4xx and 5xx):

• • These codes differentiate between client-side (4xx) and server-side (5xx) errors, enabling targeted troubleshooting and issue resolution.

• Exception rates:

• • This metric tracks application-level exceptions and crashes, providing insights into code-related issues that may compromise system stability. High exception rates may be due to unhandled exceptions or faulty logic in the application code.

• Dependency failure rates:

• • This metric identifies failures in external services or databases, highlighting vulnerabilities in system integrations and dependencies. Frequent failures in dependencies could cause service downtime and impact the user experience.

High error rates mean unhappy users and potential system crashes. Watch them closely to ensure better reliability and performance stability.

4. Resource utilization: Ensuring optimal allocation 

Microservices operate in distributed environments, requiring resource consumption monitoring. Inefficient resource allocation can cause degraded performance, application crashes, or higher infrastructure costs. To effectively monitor resource utilization, you should analyze:

• CPU utilization:

• • Elevated CPU utilization can indicate performance bottlenecks, signaling the need for resource optimization or system scaling.

• Memory usage:

• • Excessive memory consumption can lead to out-of-memory errors, emphasizing the importance of monitoring memory allocation and usage patterns. Memory leaks or insufficient memory management can cause service crashes and degrade performance over time.

• Disk I/O and network I/O:

• • Monitoring data transfer rates helps identify potential disk or network congestion, preventing performance degradation due to I/O bottlenecks. High disk I/O can be due to excessive logging or inefficient database queries, while high network I/O can indicate bandwidth limitations.

Efficient resource usage ensures stable system performance and cost effectiveness, preventing unnecessary downtime and scaling expenses.

5. Service availability and uptime: Maintaining system reliability 

High availability is crucial for microservices. Critical metrics that should be tracked are:

• Service uptime percentage:

• • This metric quantifies the reliability of microservices over a given period, providing a clear indication of system stability. Achieving 99.99% uptime is a common industry goal to ensure continuous service availability.

• Downtime incidents:

• • Logging unexpected service disruptions facilitates root cause analysis, enabling organizations to implement preventive measures.

• Health check status:

• • Real-time monitoring of service availability through health check APIs enables proactive issue resolution, minimizing potential downtime.

6. Request tracing and dependency mapping: Diagnosing performance bottlenecks 

Distributed tracing and dependency mapping are crucial for diagnosing performance issues. To achieve this, you must consider:

• Trace ID propagation:

• • This enables the tracking of requests as they traverse multiple services, facilitating end-to-end performance analysis. This helps you pinpoint slow services and optimize request paths.

• Service dependency graphs:

• • Visualizing the relationships between services aids in the identification of performance bottlenecks and inter-service dependencies. This helps you ensure service failures don’t create cascading issues.

• Slow request paths:

• • This metric pinpoints specific service interactions that contribute to performance degradation, enabling targeted optimization efforts. Slow request paths often indicate inefficient service orchestration or database latency issues.

Understanding service dependencies helps streamline troubleshooting and optimization, reducing mean time to resolution (MTTR).

7. Container and orchestration metrics: Managing containerized environments 

For microservices deployed in containers, monitoring container metrics is essential. The following metrics are vital:

• Container CPU and memory limits:

• • Ensuring adequate resource allocation prevents resource starvation, maintaining stable container performance. Exceeding allocated limits can cause container crashes, affecting service reliability.

• Pod restarts:

• • Monitoring pod restarts helps identify potential configuration issues or instability in container deployments. A high restart rate suggests resource exhaustion or misconfigured auto-scaling policies.

• Node resource utilization:

• • Monitoring cluster health ensures stable infrastructure, preventing resource contention and performance degradation. Keeping nodes healthy ensures balanced workload distribution and prevents performance bottlenecks.

Effective container monitoring ensures stable, predictable performance in dynamic cloud environments.

Leveraging ManageEngine Applications Manager for comprehensive monitoring 

Now that we’ve covered the key metrics, let’s take a look at how ManageEngine Applications Manager simplifies microservices monitoring. Applications Manager provides comprehensive microservices monitoring with real-time insights into performance, availability, and health. It empowers you to:

• Monitor response times, throughput, and error rates effectively.

• Track resource utilization across microservices and infrastructure.

• Identify performance bottlenecks using distributed tracing.

• Ensure high availability through proactive alerts and health checks.

By employing these metrics and leveraging robust monitoring tools, your organization can ensure the efficient operation of its microservices to deliver a seamless digital experience. Explore a free, 30-day trial of Applications Manager to gain comprehensive visibility into your microservices ecosystem.

The post Essential metrics for robust microservices performance monitoring appeared first on ManageEngine Blog.

What are Docker metrics?  

Docker metrics are performance indicators that provide insight into how your containers and the underlying host system are performing. These metrics can be fetched with a Docker monitoring tool that helps you answer essential questions such as:

• How is my container consuming resources?

• Is the application experiencing any bottlenecks?

• Are there any resource limits that need adjustment?

By continuously monitoring these metrics, you can proactively address issues, improve resource allocation, and ensure the reliability of your production environment.

Docker monitoring: Key Docker metrics explained  

1. CPU usage  

What it measures:

CPU usage indicates the percentage of computing resources your container is using over time. High CPU utilization can signal that your container is under heavy load, which might lead to performance degradation if not managed properly.

Why it matters:

• Performance tuning: Understand whether your application is CPU-bound.

• Resource allocation: Adjust CPU limits to avoid contention between containers.

• Troubleshooting: Identify processes that are hogging CPU resources.

2. Memory usage  

What it measures:

Memory usage metrics show the amount of RAM consumed by a container. They often include both the memory limit set for the container and the actual memory consumption.

Why it matters:

• Stability: Prevent applications from crashing due to out-of-memory errors.

• Efficiency: Ensure that memory is allocated appropriately without wastage.

• Scaling: Decide whether to vertically or horizontally scale your services.

3. Network I/O  

What it measures:
Network metrics track the data transmitted and received by your containers. These metrics include packet counts, error rates, and bandwidth usage.

Why it matters:

• Monitoring traffic: Assess whether the network throughput meets the application’s requirements.

• Security: Detect anomalies that could indicate security breaches or misconfigurations.

• Optimization: Identify network bottlenecks and improve container communication.

4. Disk I/O  

What it measures:
Disk I/O metrics monitor the read and write operations to storage within the container environment. This is especially important for data-intensive applications.

Why it matters:

• Performance: High disk latency can lead to slow application responses.

• Capacity planning: Plan for storage expansion or optimization.

• Diagnosis: Pinpoint disk-intensive processes that may require tuning.

Tools and techniques for monitoring Docker metrics  

Several tools are available that integrate seamlessly with Docker to monitor these metrics. Some of the popular options include:

• ManageEngine Applications Manager:
  ManageEngine Applications Manager provides robust Docker monitoring capabilities, addressing the challenges of maintaining visibility into dynamic containerized environments.

• Docker stats command:
  A built-in tool that provides real-time statistics of container performance. Running docker stats in your terminal gives you an overview of resource consumption across containers.

• cAdvisor (Container Advisor):
  An open-source tool by Google that collects, aggregates, processes, and exports resource usage and performance data from running containers.

• Prometheus and Grafana:
  Prometheus collects time series data, and Grafana provides powerful dashboards for visualization. This combination is popular for monitoring dynamic, containerized environments.

Best practices for monitoring Docker metrics  

Here are some of the best practices to follow when it comes to approaching Docker monitoring:

1. Establish baselines:
   Understand what “normal” looks like for your applications. Establishing baselines helps you identify anomalies quickly.

2. Set alerts and thresholds:
   Configure alerts to notify you when resource usage exceeds predefined limits. This proactive approach prevents minor issues from escalating into major outages.

3. Leverage dashboards:
   Visual dashboards provide a clear and concise view of your system’s performance. Use them to monitor trends over time and diagnose issues.

4. Monitor across layers:
   In addition to container-specific metrics, monitor the host system and network infrastructure. This holistic view can help isolate performance issues that might not be apparent at the container level.

5. Automate and integrate:
   Incorporate monitoring into your CI/CD pipelines. Automated tests and monitoring can catch issues early, ensuring a smoother deployment process.

Understanding Docker metrics is essential for anyone working with containerized applications. These metrics not only help in maintaining system health but also provide actionable insights for performance tuning, scaling, and troubleshooting. Whether you’re just starting with Docker or managing a complex production environment, investing time in monitoring will pay dividends in stability and performance.

By leveraging the right Docker container monitoring tools and following best practices, you can ensure that your Docker environment remains resilient, efficient, and ready to support your growing applications.

Docker monitoring with Applications Manager 

ManageEngine Applications Manager effectively addresses the inherent challenges of Docker monitoring, which arise from the dynamic and ephemeral nature of containers. It provides robust capabilities to maintain critical visibility into these environments.

Key aspects of Docker monitoring with Applications Manager:

• Comprehensive visibility:

• • It offers deep insights into the health and performance of Docker hosts and containers.

  • This includes monitoring key metrics like CPU usage, memory consumption, network I/O, and disk I/O.

• Agentless monitoring:

• • Applications Manager simplifies Docker monitoring by using an agentless approach.

  • This reduces the overhead of installing and maintaining agents on individual containers, streamlining the monitoring process.

  • It utilizes the Docker Remote API to gather performance data.

• Automated discovery and dependency mapping:

• • The tool automatically discovers Docker containers and their dependencies, providing a clear understanding of how they interact.

  • This is crucial for identifying potential bottlenecks and troubleshooting performance issues.

• Real-time monitoring and alerting:

• • Applications Manager collects and analyzes Docker monitoring metrics in real time.

  • It provides alerts and notifications when performance thresholds are breached, enabling proactive issue resolution.

• Application performance in Docker:

• • Beyond basic container metrics, it monitors the performance of applications running within Docker containers.

  • This allows for a holistic view of application performance in the containerized environment.

• Reporting and analytics:

• • The tool provides comprehensive reports and analytics to track Docker performance trends and identify areas for optimization.

In essence, ManageEngine Applications Manager offers a comprehensive solution for Docker monitoring, providing the visibility and control necessary to ensure the health and performance of containerized applications. Try it today!

The post Understanding Docker monitoring: A comprehensive list of key Docker metrics appeared first on ManageEngine Blog.

How does this recognition add value to your monitoring experience?

The apps that are listed as Microsoft Intune partner productivity apps are compliant with Microsoft’s security settings. These settings allow admins to protect their organizations’ data integrity by restricting certain data sharing actions across applications. Applications Manager’s integration with Microsoft Intune’s SDK protects the monitoring console from security breaches across organization-owned and employee-owned devices that carry critical app performance data and server credentials.

This integration offers the following security restrictions:

• PIN protection: Configure a unique PIN for Applications Manager in every device to avoid unauthorized logins.

• Copy and paste restriction: Restrict copying or pasting server data from Applications Manager without admin access.

• Screenshot restriction: Restrict snapping the screen when Applications Manager is on display to avoid unauthorized data publication.

• Navigation controls: Control switching and redirecting to other applications from the Applications Manager mobile app.

This listing officially consolidates our compliance with critical app protection policies and regulations of Intune. You can download the integrated version from Google Play and the Apple App Store to leverage a secure mobile APM experience across your organization.

The post Applications Manager is now an Intune partner productivity app! appeared first on ManageEngine Blog.