CPU Load Isn’t A Performance Indicator


Let me clarify.

While CPU Load has value, it’s interpretation depends on a thorough understanding of what it’s actually indicating and why. It’s no longer meaningful as performance indicator, because it suggests a physical CPU’s ability to keep up with some processing load. This is easily grossly misinterpreted and misunderstood.

Why is this a problem?

Let’s start with jobs that are run during an idle state. Or, more specifically, the niceness of a process.

There are processes that, by design — and a lot of them — are de-prioritized and will use resources only if they’re available.

Anyone recall the SETI@home project? Remember how it would happily run “in the background”? It was designed with the priority concept where it would have some quantity of work that it needed to work on, but because it was a background job, if you wanted any resources at all, it would happily step out of the way and yield the CPU load.

It wasn’t the first. There are loads of system-level tasks that do exactly the same thing. Rather than sit idle, doing nothing at all until you ask it for something, there are loads of background tasks that it’s doing.

The classic interpretation of “CPU Load” or processor load and such as a metric indication of system performance would’ve suggested that the system load was too high and that it needed additional resources.

But it wasn’t.

It was just running exactly as it was designed and doing the work that it was meant to and would happily put things into a wait state if you wanted to do anything else.

In fact, even when effectively idle, a CPU will run at precisely the clock rate that it was intended to. Long ago, Unix started presenting a CPU Load Average — an indicator of the average number of processes that were waiting for the CPU (called a Wait State) during a the last one, five, or fifteen minutes.

These, too, will return an extremely high figure should a lower priority process want to run.


Also, “Priorities”. They work.

Nowadays, we have much the same challenge — working to change perspectives, helping people to unlearn what they’ve learned — but on containerized workloads.


Wrong perspective. The right perspective is to ask yourself instead, “How’s my app’s performance?” Is it responsive to requests that it receives? Ask yourself, “Have I taken every conceivable step I can to improve its performance?” You have? All of them that you can? Are you sure?

Have you also taken steps to fundamentally shrink its instruction set so it’s not carrying around all of that unneeded bloat? I’m not referring to code-bloat — code-bloat is a separate problem there, too — but, more foundational than code: have you reduced the CPU instruction set?

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