Everybody hates it when they have to wait for an application to load—or when an application doesn’t load at all. And if this happens with your application, you’re not just losing business but also losing brand value. Most applications today are online. So servers play a crucial role in keeping applications up and running.
Application performance is directly proportional to server performance. Hence, it’s very important to monitor and improve server performance. Server performance has different aspects. In this post, we’ll go through different metrics that help evaluate server performance and how we can improve these metrics. Then we’ll discuss how important server performance monitoring is and how to go about it.
Server performance, in general, is a measurement of how well the server is performing. But what defines “well”? Every server is built, configured, and used for specific tasks. For example, database servers are responsible for storing, manipulating, and transacting data; mail servers are used to handle and deliver emails; and so on. A server can be considered to be performing well when it’s providing the service it’s supposed to and when it’s supposed to.
Server performance measurement is a combination of multiple metrics. To decide whether a server is performing well or not, you need to measure different server performance metrics and then come to a conclusion. Now let’s look at some of the most important server performance metrics and how to improve them.
Throughput is the measure of how many requests a server processes in a given unit of time. The general value of time used in throughput calculation is a second. But this can vary based on use case. For example, if a server processes 100 requests in a second, then the server’s throughput is 100. But in some cases, it might not be able to calculate throughput each second. In such cases, you can use average throughput. Average throughput is the ratio of the measure of requests processed over a period, by that period of time.
So if 30,000 requests were processed in 10 minutes, then the average throughput is 30,000 requests/10 minutes—i.e., 50 requests per second.
You can optimize throughput by reducing latency. One of the most common types of latency that affect throughput is network latency. You need to analyze the cause of high latency. It could be hardware, memory, routing, etc. Once you fix the issue that’s causing high latency, throughput will automatically increase.
CPU usage is the amount of time the CPU is being used. We generally calculate CPU usage as a percentage. Hence, CPU usage can be defined as the percentage of time that the CPU is being used to complete its tasks.
Some of the common reasons for high CPU usage are as follows:
And some of the common ways to optimize CPU usage include the below:
Server utilization is the percentage of server resources that the server is using to process requests. High server utilization is bad because it affects the server hardware and also affects the application.
Here are some of the most common reasons for high server utilization:
A spike in legit traffic is good for business if your server is capable of handling the spike. So it’s not really a problem that needs a fix. Some of the ways to improve server utilization include the following:
Server uptime is the amount of time the server is running and providing the desired service. You can also calculate uptime as a percentage as follows:
Server uptime = (Amount of time the server is running and providing service / The total amount of time it is expected to run and provide service) x 100
The main factors that affect server uptime are hardware, the operating system, and software. To maximize server uptime, you need to use high-quality hardware, make regular updates to your software, and build a robust architecture. Another important aspect to take care of in order to maximize uptime is security. There are many cases where hackers have hacked a server, and the organization had to bring the server down to limit the risk. One well-known incident is when NASA was hacked and they had to shut down their computers for 21 days to limit the risk and determine the extent of the attack.
Server response time is the measure of time from when a client sends a request to the server to the time when the server sends a response. Response time is one of the most important metrics that affect user satisfaction. The benchmark for a good response time will vary from one use case to another.
A common confusion is that response time is the time taken for a server to complete its task. A simple application can process a request in milliseconds, whereas an application that has to complete gigabytes of data will take more time. But a response doesn’t need the server to complete processing the request. So response time is the time when a server sends a response to the client.
Here are some of the common ways to improve server response time:
The error/failure rate is the percentage of requests the server was not able to successfully process despite being up and running. Error/failure rate can be calculated as follows:
(Number of requests the server was not able to complete request processing for / Total number of requests) x 100
Some of the common causes for a server not being able to complete processing requests include the below:
The general approach to decrease error/failure rate is to identify the cause of the error and fix the issue. For example, let’s consider an e-commerce website. An e-commerce website hosted on a server performs well usually. But when the website puts up offers, the error rate is high. One of the reasons for this could be that when there are offers, more people are visiting your website, and due to high traffic, the server might not have enough resources to process all requests. You need to check if your resources are enough to process requests from high traffic.
Another example could be if you’re seeing a high error rate when an update was pushed to production. The error could be due to a bug in code or due to an exception. Proper logging would help you identify such issues.
The time to repair metric is not a direct measure of server performance. It’s the average time taken for the relevant teams to solve server problems. We all wish for an ideal world where there are no problems on production servers. But we don’t live in an ideal world. Problems will come up, but what’s important is how quickly they’re solved.
One of the main reasons for a higher value of time to repair is a lack of immediate identification of the problem. This can be improved by implementing proper server management and monitoring solutions.
These are some of the most important metrics that together evaluate server performance. When each of these individual metrics is improved, the overall server performance will surely improve.
For all the above metrics, there’s one common thing that you have to do to improve those metrics—monitoring. Server performance and metrics monitoring is the most important part of server management. It goes without saying, but if you don’t monitor, you can’t easily and quickly identify issues and can’t fix them quickly. In this era, we can’t afford to have low-performing servers, even for a couple of minutes. Hence, it’s important to continuously monitor server performance and get alerts when issues are identified.
Implementing a server performance monitoring system from scratch can be difficult. And most importantly, it’s going to take time for continuous version updates and to make it the perfect solution. What you can do instead is use an existing solution. There are many services and products for server management. Netreo is one of them. Netreo offers a server management solution and helps with monitoring and alerting across the board. It has great features for monitoring and alerting and supports multiple platforms. If you’re looking for a complete server management solution, you can request a demo today!
This post was written by Omkar Hiremath. Omkar is a cybersecurity analyst who is enthusiastic about cybersecurity, ethical hacking, data science, and Python. He’s a part time bug bounty hunter and is keenly interested in vulnerability and malware analysis.