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Server Monitoring Best Practices: 9 Tips to Improve Health and Performance

Businesses that have mission-critical applications deployed on servers often have operations teams dedicated to monitoring, maintaining, and ensuring the health and performance of these servers. Having a server monitoring system in place is critical, as well as monitoring the right parameters and following best practices.

In this article, I’ll look at the key server monitoring best practices you should incorporate into your operations team’s processes to eliminate downtime.

What Is Server Monitoring

Server monitoring is a process that enables organizations to gain visibility into the state of the servers hosting their applications. With server monitoring, you can keep an eye on system availability, security, and performance through multiple metrics and logs, receive notifications when something goes wrong, and rapidly spot and resolve any problems. It serves to ensure that servers are performing optimally and helps you take proactive actions to prevent downtime.

Monitoring servers is not as straightforward as ensuring availability. Servers could be up and running and responding to ping requests—even when the applications and services hosted in them are facing downtime. There could be scenarios where the services are running, but user experience is impacted due to delays. A thorough server monitoring strategy involves keeping track of all pertinent parameters of a server and fixing any potential problems.

A centralized server monitoring solution can assimilate the metrics collected from your server resources—irrespective of whether they are hosted on-premises or in the cloud—and provide meaningful insights into the state of your server’s health.

Why Is Server Performance Monitoring Important?

Servers are the backbone of your IT infrastructure. Any outages impacting your servers or applications can bring down your business and cause long-lasting negative impacts. The same goes for server performance issues. Customers today have so many choices at their fingertips—it is nearly impossible to retain them if your apps are plagued with performance issues.

Having a well-defined and managed approach to server performance monitoring becomes crucial to proactively identify and flag any issues and resolve them fast. Server performance monitoring, when done right, provides a number of tangible benefits to your business:

  • Prevent downtime, meet your SLAs, and avoid financial penalties.
  • Offer better service quality, uptime, and reliability, thereby gaining customer loyalty and repeat business.
  • Right-size your servers for optimal return on investment.
  • Proactively detect issues to help prevent costly emergency repairs and downtime.
  • Make informed decisions regarding long-term IT strategy, be it capacity planning, server upsizing, or reconfiguration.

Overall, server monitoring becomes critical for ensuring the reliability, efficiency, and stability of your business. The right monitoring tools play an important role in reducing unexpected downtime, stabilizing system performance, and ensuring profitability in the long run.

What Resources Should You Monitor for Server Health and Performance

There are several metrics that indicate the status of your server performance. These include, but are not limited to, metrics associated with availability, capacity, load tolerance, and application health.

The metrics to be monitored depend on your business priority and specific use case requirements. Some metrics at the infrastructure and application levels might need to be monitored for all environments; however, some other metrics could be application-specific.

Let’s take a look at some of the key metrics to be considered while designing your monitoring strategy:


  • Availability
  • Hardware utilization
  • Load

Container-level (operating system)

  • vCPU
  • Disk usage
  • I/O
  • Swap use
  • Uptime

Platform level

  • Container instance count
  • Container churn
  • Container combined uptime

Application level

  • Requests per second
  • Latency
  • Throughput
  • Average response time
  • Error rate
  • Thread count
  • Memory consumption
  • JVM (GC & heap)

Resource level

  • Open files
  • Process count
  • Permissions
  • Network connection

Security level

  • Data breach
  • Intrusion detection
  • Vulnerability & malware detection
  • Unauthorized access
  • Log pattern

Along with server-side metrics, it’s also important to keep an eye on client-side metrics such as page load time, CPU idle time, and render time.

You can also instrument your applications to monitor application-specific metrics. Depending on the workloads and the deployment architectures, there would be additional networking and security parameters as well that need to be monitored.

We delved deeper into these metrics in our article on server performance metrics.

In addition to metrics, it is also recommended to monitor server logs, as they provide detailed information about server events and help understand why a specific metric might be askew. Logs provide details on what happened at a particular point in time, give insights into the root cause of the problem, and aid with debugging. Together, logs and events help fast-track the troubleshooting process.

The metrics and logs should trigger downstream alerts to ensure effective server monitoring. Alerts should get triggered when a threshold specific to your metrics/logs is violated. Unless alerts are configured to inform the operations team and stakeholders in a timely manner about outages, mitigation timelines will be delayed, thereby impacting your business operations.

Server Monitoring Best Practices

Best practices ensure that you are using the information from metrics and logs to maintain stability, security, and optimal performance of your systems. Let’s explore some of these server monitoring best practices in detail.

1. Configure the Right Metrics

Not all the metrics we defined earlier apply to all environments. Depending on the nature of the application, server architecture, and specific organizational requirements, you will need to zero in on the relevant performance metrics. You should consider the metrics from the viewpoint of the users as well; for example, the geographical location from which users are accessing the application means that the associated latency should definitely be monitored.

Other than basic server availability and uptime metrics, make sure you’re including the right metrics for insights into performance bottlenecks. For example, CPU/memory usage patterns over a period of time can identify the peak usage window and help you integrate autoscaling patterns to avoid issues.

The metrics included should also align with the required service levels
of your application, usually measured by an SLA. So, if a small delay is tolerable for your application but not errors, then you need to give priority to error rate metrics. If your application has stringent availability requirements, say, banking and transactions, then you might need to increase the frequency of your uptime and availability metrics.

Also, when considering availability metrics like average response time, don’t forget to factor in the outliers that could impact accuracy. It might make sense to keep track of these outliers separately and identify any patterns. This would demand a monitoring tool that can track metrics with fine-grained accuracy and filter out this type of detail.

2. Correlate Monitoring Data

A performance bottleneck impacting your server could be due to one or more limitations flagged by their respective metrics. There is also the possibility of one metric impacting the other, leading to a snowball effect. Because of this, your server monitoring system should be capable of providing a holistic view of all these metrics and help determine any correlation between them for effective troubleshooting.

Some metrics like error rates, i.e., the percentage of failed requests, could indicate a server bottleneck or an application misconfiguration. The information from your monitoring tool should help identify the root cause of a problem. Higher application response times could be related to server issues but also to delays impacting the network at large. Since the monitoring tool will have metrics collected from various servers in the same network, this type of environment-wide impact can be easily spotted and rectified.

When it comes to performance bottlenecks, you might have to look at different metrics at the same time to zero in on the problem. You will have to check the CPU usage and system load to see if the constraint is related to processing capacity. You’ll need to take into account different memory metrics to understand memory usage patterns, i.e., the memory currently being used by different processes in the system, the memory cache available for use if required, etc.

Metrics related to disk I/O, such as throughput and latency, can give you critical information pertaining to performance bottlenecks. Throughput tells you the amount of data that can be processed within a given time and is measured in bits/bytes per second. Latency, on the other hand, quantifies the time taken for data to be transferred; this is often measured in milliseconds. Systems could become sluggish or unresponsive to data read and write operations due to disk performance limitations.

3. Automate the Monitoring Process

Configuring metrics in a monitoring tool is just one step of the process. Having a well-defined and automated monitoring process will help you run the systems smoothly. A manual monitoring approach will not scale in large-scale deployments. Irrespective of how large your operations team is, it’s a waste of man-hours for someone to log in and keep an eye on the monitoring dashboards all the time. If the process is not automated through alerts, you run the risk of errors and issues not being flagged to the right stakeholders at the right time.

Organizations should also have an operations process in place that should get triggered once an alert is received. It should also assimilate lessons learned from past problems—as well as information regarding environment-specific details—so that there are no delays in taking quick action. You can additionally create a repository of remedial scripts for some of the more common issues, with clear documentation on how to use them for specific alerts. Some advanced monitoring tools can recommend remedial actions based on the nature of an alert and suggest an automated remedial process.

4. Set Up Detailed Alerts

Alerts play a critical role in the flow of operations once a performance issue is detected, so pay careful consideration to your requirements when finalizing what information should be included. They should have some mandatory details such as the source of an alert, the severity of the issue, the trigger point of the alert, and to who the alert should be directed.

Not all alerts need to be sent to everyone. Some alerts should only be sent to teams focused on specific areas, for example, security alerts should only go to the security team. Similarly, critical alerts about outages with a widespread impact should be sent to business unit leaders and the operations team.

Along with the frequency of monitoring, the frequency of alerting is also important. The last thing you want is for your operations team to drown in a flood of alerts due to a lack of proper planning and configuration. Remediation guidance, if available, can also be included as part of the alert content.

5. Create Meaningful Dashboards

Visual representations of monitoring data using dashboards are an easy way of getting a bird’s-eye view of your server infrastructure performance and health. Dashboards provide a simpler way for correlating and identifying the root cause when a performance bottleneck could be attributed to multiple sources. You can easily pinpoint where the issue is by analyzing the dashboard rather than going through pages of raw data.

Naturally, proper planning is also necessary while developing dashboards. Configuring all your metrics in all dashboards might look visually appealing, but this won’t serve the purpose of server monitoring and root cause analysis. Carefully select the right metrics appropriate for each team, and create business unit/application-specific dashboards. The dashboards should come out as uncluttered, meaningful, and organized—and aligned with the purpose they were created for.

6. Align Notification Channels

Alerts can serve their purpose of triggering the remediation process only if they are delivered in a timely manner to the right people. That’s where choosing the right notification channel and aligning them with human business processes becomes crucial.

It is important that alerts are routed to a monitored incident response system with integrated notification channels. Too many alerts can also lead to some being ignored. Because of this, organizations should carefully consider alert frequency, their level of significance (P1, P2, P3), and the SLA associated with the alert. As there is often millions riding on production systems, getting alerts right to flag any issue and having a strong production monitoring discipline in place has a significant monetary impact.

Alerts sent to an incident response system will have multiple primary and secondary notification channels integrated with it. So, if you have a paging system in place, it makes sense to send a page along with an email to ensure the issue is taken care of. Additional options include chat integration with Slack/Teams, phone calls, SMS, etc.

7. Platform Optimization

When a system is elastically scaled, it should be continuously monitored for right-sizing. This will help with cost and performance optimization in the long run. Configuring your monitoring tool is not a one-time activity. You need an iterative process in place to fine-tune the configuration of the tool and the associated operational procedures.

When systems are consistently under-utilized, this should lead to a right-sizing exercise to reduce costs. When they are overutilized, it could eventually lead to performance bottlenecks. The monitoring platform should be capable of providing these insights. Analyzing the alert patterns over a given period of time will tell you about the possible areas of false alerts or help identify metrics that are redundant. A delay in remediation, for instance, could indicate an ineffective notification channel that needs to be replaced/updated.

Timely updates of operations procedures based on the monitoring process should be a collective responsibility. As your process matures, you should focus on optimizing it to sharpen the outcome. You can also discover monitoring blind spots based on the analysis of past outages and then include additional metrics as required. It helps to create a commonly accessible knowledge base of these incidents and integrate it with your monitoring tool.

8. Log Monitoring

Log monitoring is the process of collecting and analyzing log data from sources like operating systems, hardware, applications, databases, etc. While metrics could indicate a deviation from the normal behavior of systems, logs give more detailed insights about what led to that behavior. Metrics can also be triggered from a specific log pattern, for example, audit or security logs. On receiving alerts based on these metrics, you can further drill down into the metrics graphs to the corresponding logs to obtain additional details.

Logs contain detailed information about various aspects of your system like user interaction, system operations, application errors, service issues, etc. Collecting, analyzing, and correlating logs helps fast-track the root cause analysis of issues impacting your system. It also helps in the case of security breaches by learning behavioral patterns and identifying the lateral movement of threats. The monitoring tool an organization is considering for enterprise systems should ideally have built-in capabilities to consolidate and analyze logs to provide end-to-end visibility of system health.

9. Build vs. Buy

There is a common misconception that buying an out-of-the-box solution is not a good idea, as it may not fit in well with the specific needs of your organization. However, you need to take into account how complex it is to build your own solution—it’s a major project that needs a heavy investment of time and resources. You might also need a dedicated team of people to maintain and upgrade the code base as your server infrastructure evolves toward a hybrid and multicloud model.

The recommended approach is to identify a monitoring solution from the market that closely matches your requirements and that is also customizable. You need to look at the openness of the solution in terms of how easy or difficult it is to integrate it with your existing and future server landscape.

Definitely throw cloud in the mix, as that is where the world is headed. Even if your current server landscape is on-premises, consider future expansion to the cloud and select a tool that can support all deployments.

In our article on server monitoring tools, we compare the top solutions available in the market and provide helpful tips on choosing the right one for your business.

Server Performance Monitoring with Sematext

With all the nitty-gritty involved, server performance monitoring is not a straightforward task—especially for large organizations. Identifying the right metrics, the right tool, and the needed operational process involves detailed research, planning, and meticulous execution.

Sematext Monitoring is an infrastructure monitoring solution with server performance monitoring capabilities that helps simplify this process. It can take care of the infrastructure, servers, containers, databases, and network monitoring for your multicloud and hybrid server environments.

Sematext’s ability to correlate metrics from different sources, generate timely alerts, and integrate with different systems and applications make it a solution of choice for both big and small enterprises. It can monitor all relevant server metrics, presenting them via out-of-the-box, yet fully customizable dashboards for reporting and capacity management. In the case of large-scale infrastructure, it makes the life of your monitoring team easy by letting them filter your servers using tags, disks, hosts, network interface, etc.

Further, as part of the Sematext Cloud suite, Sematext Monitoring seamlessly integrates with Sematext Logs, a log management tool that enables you to aggregate logs from across your entire infrastructure. In nutshell, Sematext provides a centralized solution for server performance monitoring, helping you correlate bottlenecks with logs and metrics to easily identify the best course of action to resolve them.

Try out Sematext with the 14-day free trial! The video below shows you exactly what you’ll get with Sematext Monitoring.

Author Bio

Jean-Christophe Dansereau

Jean-Christophe Dansereau is a Canadian software engineer and tech writer, specializing in backend and cloud engineering. He helps tech companies develop and scale applications and write technical blogs to allow organizations to communicate their message.

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