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Linux Performance Tuning

Linux performance tuning is the disciplined work of measuring a Linux workload, finding the resource or code path that limits it, and changing the smallest relevant control. It covers CPU scheduling, memory pressure, storage and network behavior, application profiling, and validation under a repeatable workload.

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Don't Panic — Linux Performance Tuning

Linux performance tuning is the disciplined refusal to poke a kernel setting because a graph looked grumpy. A Linux application runs instructions and makes system calls; the kernel then schedules work, manages memory, moves files, and sends packets. A delay can begin anywhere along that route, which is inconvenient but at least prevents boredom.

Start with the work that matters. Throughput is completed work per unit of time. Latency is the time for one operation. Utilization tells you how busy a resource was, while saturation tells you that demand had to wait. Those are not four names for the same anxious feeling. A system can be busy and healthy, or quiet and still make a request wait in a queue.

The first surprise is that Linux load average is not a CPU percentage. It counts runnable tasks and tasks in uninterruptible sleep, so high load with idle CPU can point at blocked work rather than exhausted processors. Pressure stall information, or PSI, adds another useful clue: it reports time lost while tasks cannot progress because CPU, memory, or input and output resources are contested. The machine is not being mysterious. It is being specific in several places at once.

The practical route is broad evidence first, detail later. Compare workload latency, throughput, and errors with CPU, memory, storage, network, and pressure signals. Then narrow the question to a process, control group, device, socket, or code path. Metrics tell you where to look; a profile estimates where execution time gathers; tracing records selected events with timing and context. Turning on everything at once is a fine way to produce a large collection of facts about your confusion.

A tuning control always moves a tradeoff. CPU affinity can improve locality and also strand capacity. Lower swappiness changes the balance between anonymous memory and filesystem cache. Larger dirty limits can absorb a write burst and later create a longer writeback stall. The useful change is one that improves the chosen workload outcome while errors, tail latency, pressure, and resource use remain acceptable.

Read the Intro for the complete path through CPU, memory, storage, and network behavior. Use Slides for the map of signals and decision points. Keep the Cheatsheet nearby when comparing metrics and controls. The Reference tab leads into the kernel and Red Hat documentation when a hypothesis needs exact semantics. That is the whole arrangement: measure, localize, change one thing, and make the result earn its keep.

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