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Whether an application is running on a server or a local machine, monitoring CPU utilization and CPU load is essential for optimizing performance. While CPU utilization and load might sound similar, they’re actually quite different.
This article will explain the differences between these two important metrics, how to monitor CPU load with common commands, the impact of high CPU load, and how to bring it under control for improved system performance.
CPU utilization is the percentage of work the CPU is handling to manage tasks. CPU load, on the other hand, is a measurement of how many processes are being executed or waiting to be executed by the CPU.
top provide the value of CPU load averages—i.e., the average number of threads actively using or requesting to use CPU over the last 1, 5, and 15-minute periods. High load averages indicate an overloaded CPU with too many processes. For example, a CPU with a load average of 1 is running at full capacity, whereas a completely idle CPU has a load value of 0.
However, this metric scales with the CPU cores: The more cores installed on the system, the more tasks it can handle in parallel.
For example, for a CPU with 4 cores, the accumulated load average will be 4, since each core can handle a load value of 1. In this scenario, even if one of the cores is running at 100% capacity, the CPU would only be loaded with one quarter of its potential load.
Now, if the load becomes greater than the number of cores installed, then the processes would start to queue up to use the CPU.
As noted above, CPU load is averaged over the previous 1, 5, and 15 minute periods. But there are other metrics that also help us identify the CPU load, such as:
Generally, a high CPU load doesn't negatively impact a system’s performance, as long as it isn’t a long-term occurrence. But running a CPU at 100% capacity for extended periods can have mild to severe impacts on system performance.
A few possible issues are listed below:
Different commands help monitor the system’s load over different periods. Usually, a smaller number is better, as a higher number indicates an overloaded machine.
The next section will cover some of the commands that make it easy to monitor the CPU load averages.
top command displays the dynamic statistics of a running Linux system in real time. It’s one of the most-used commands for monitoring system performance. The first half of the output of the
top command contains important system metrics, while the second part displays statistics about a self-updating list of processes that are currently being managed by the Linux kernel.
top command will create an output similar to the one seen in the figure below:
The first line of this output displays the uptime, the total number of active users logged into the system, and the load averages of the CPU for the last 1, 5, and 15 minute intervals.
For example, the above output shows the load averages as 0.13, 0.40, and 0.21. As stated earlier, to properly interpret these numbers, it’s important to know how many cores the CPU has. The above output is from a single-core machine, so the load average is within the acceptable limit, as all of the load averages are less than 1.0. Even if there’s a spike in a 1 and 5-minute duration, as long as the load average of the 15-minute duration is within the limit, there shouldn’t be an issue.
The uptime command is also useful for viewing the load average of the system. This command displays the current system time, the uptime of the machine, the number of users currently logged into the system, and the load averages for the last 1, 5 and 15-minute durations.
uptime command will generate an output similar to the one shown below:
The above output is very similar to the first line of the output of the
top command. The load averages are displayed in the same format, and the value of the load averages are 0.53, 0.56, and 0.24. Since this is the output of a single core machine, the load averages are still under the limit, as they are under 1.0. This value is always adjusted according to the number of cores in the system.
ps command is a flexible and widely used tool for identifying the processes running in the system and the number of resources they’re using to run. This command can show different outputs according to various options.
ps command will generate an output like the one shown below:
This output displays basic information about the processes running, but it can be customized with options provided by the
ps command to yield more details.
For example, we can view and sort which processes are using the most CPU by running the following command:
ps -eo pcpu,pid,user,args | sort -k 1 -r | head -10
This will result in an output like the one shown below:Fig 4: The top 10 most CPU-consuming processes
ps command doesn’t display the load averages of the system, instead, it’s used to troubleshoot the cause and find the processes that are causing the high CPU load. For example, if a process is using 100% of the CPU, the other processes will have to wait for the CPU and the load on the CPU will increase.
This command also helps to identify the processes that are being spawned repetitively or are in a zombie state.
Below are some common fixes to reduce high CPU load:
The CPU load is an important metric that needs to be monitored regularly to ensure that the system is running smoothly. This metric is generally measured in load averages, but there are some other measurements that indicate the amount of load on the CPU.
Luckily, there are a number of useful commands that can help identify and monitor the CPU load. The
uptime commands help to directly monitor the load averages of the CPU, while the
ps command is used to identify the processes that are causing the high CPU load.
High CPU load can be an indicator of several problems, and there are various common fixes available to reduce the high load and optimize CPU performance.
The top command can become one of the most useful tools in your arsenal. Learn more about the most-used commands, understanding the top UI and metrics.➤
Understanding network usage in processes is the key to learning and fixing potential problems related to it. Learn more about Linux network issues, the key metrics to be tracked, the tools to be used, and common solutions.➤
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