A system administrator's guide to improving Linux performance.
Linux is a versatile and powerful operating system that can be tweaked to meet the specific needs of a given workload. By optimizing the operating system for performance, a system administrator can squeeze every last drop of performance out of a Linux server.
In this guide, we will cover some of the most common performance optimization techniques for Linux. We will start with a brief overview of the Linux kernel and its architecture, then move on to performance-related kernel parameters. After that, we will discuss some common performance optimization strategies, including those for CPU, memory, I/O, and networking.
The Linux kernel is the heart of the Linux operating system. It is responsible for managing all the resources of the system, including CPU, memory, I/O, and networking.
The kernel is a complex piece of software, and it can be difficult to understand how it works. However, a basic understanding of the kernel's architecture is necessary for performance tuning.
The Linux kernel is divided into several different layers. The lowest layer is the hardware, which consists of the CPU, memory, I/O devices, and so on. Above that is the kernel itself, which is responsible for managing all the resources of the system.
The kernel is divided into several different subsystems, each of which is responsible for managing a specific type of resource. For example, the memory management subsystem is responsible for managing memory, the networking subsystem is responsible for managing networking, and so on.
Each subsystem has its own set of kernel parameters that can be tuned to improve performance. In the next section, we will discuss some of the most important kernel parameters for performance tuning.
There are many kernel parameters that can be tuned to improve performance. In this section, we will discuss some of the most important ones.
The swappiness parameter controls how aggressively the kernel swaps out memory pages. A higher value means that the kernel will be more aggressive about swapping out memory pages, which can improve performance in some workloads. However, it can also cause performance problems in other workloads.
The default value is 60, which is a good starting point. However, it is worth experiment with different values to see what works best for your workload.
The transparent_hugepage parameter controls whether or not the kernel uses transparent huge pages. Transparent huge pages can improve performance in some workloads, but they can also cause performance problems in other workloads.
The default value is always, which means that the kernel will use transparent huge pages if they are available. However, it is worth experiment with different values to see what works best for your workload.
The vm.dirty_ratio parameter controls the maximum percentage of memory that can be dirty before the kernel starts writing it out to disk. A higher value means that more memory can be dirty before the kernel starts writing it out to disk, which can improve performance in some workloads. However, it can also cause performance problems in other workloads.
The default value is 20, which is a good starting point. However, it is worth experiment with different values to see what works best for your workload.
The vm.dirty_background_ratio parameter controls the maximum percentage of memory that can be dirty before the kernel starts writing it out to disk in the background. A higher value means that more memory can be dirty before the kernel starts writing it out to disk in the background, which can improve performance in some workloads. However, it can also cause performance problems in other workloads.
The default value is 10, which is a good starting point. However, it is worth experiment with different values to see what works best for your workload.
The vm.dirty_expire_centisecs parameter controls the amount of time (in 100ths of a second) that a page can be dirty before the kernel starts writing it out to disk. A higher value means that a page can be dirty for a longer time before the kernel starts writing it out to disk, which can improve performance in some workloads. However, it can also cause performance problems in other workloads.
The default value is 300, which is a good starting point. However, it is worth experiment with different values to see what works best for your workload.
The vm.dirty_writeback_centisecs parameter controls the amount of time (in 100ths of a second) between writebacks of dirty pages to disk. A higher value means that there will be more time between writebacks of dirty pages to disk, which can improve performance in some workloads. However, it can also cause performance problems in other workloads.
The default value is 500, which is a good starting point. However, it is worth experiment with different values to see what works best for your workload.
There are many different techniques that can be used to optimize CPU performance.
One common technique is to use a CPU profiling tool to identify hot spots in the code. A hot spot is a section of code that is executed more frequently than other sections of code. By Identifying and optimizing hot spots, a significant performance improvement can be achieved.
Another common technique is to use a task scheduler to distribute the workload evenly across all of the available CPUs. This can improve performance by reducing contention for resources.
There are many different techniques that can be used to optimize memory performance.
One common technique is to use a memory profiler to identify areas of memory that are being used excessively. By Identifying and optimizing these areas, a significant performance improvement can be achieved.
Another common technique is to use a memory allocator that is specifically designed for performance. The glibc malloc allocator is a good choice for most workloads.
There are many different techniques that can be used to optimize I/O performance.
One common technique is to use an I/O scheduler to schedule I/O operations in a way that maximizes performance. The deadline I/O scheduler is a good choice for most workloads.
Another common technique is to use a readahead mechanism to prefetch data into memory before it is needed. This can improve performance by reducing latency.
There are many different techniques that can be used to optimize networking performance.
One common technique is to use a network scheduler to schedule network traffic in a way that maximizes performance. The pfifo_fast network scheduler is a good choice for most workloads.
Another common technique is to use a network accelerator to offload work from the CPU to a dedicated hardware device. This can improve performance by reducing CPU utilization.
In this guide, we have covered some of the most common performance optimization techniques for Linux. By tuning the operating system for performance, a system administrator can squeeze every last drop of performance out of a Linux server.
https://access.redhat.com/documentation/en-us/red_hat_enterprise_linux/6/html/performance_tuning_guide/index
https://www.kernel.org/doc/Documentation/sysctl/vm.txt
https://www.kernel.org/doc/Documentation/filesystems/proc.txt
https://www.kernel.org/doc/Documentation/block/deadline-iosched.txt
https://www.kernel.org/doc/Documentation/block/cfq-iosched.txt