Moving from CPU Utilization to Pressure Stall Information with Linnix

observability

Many of us have been conditioned to treat CPU utilization percentages as the primary signal for resource exhaustion. This is a common misconception. A CPU at 100% is often just working efficiently; it is not necessarily stalling. The real metric of interest is resource contention. Linnix takes a different approach by leveraging eBPF to monitor Pressure Stall Information (PSI). For those unfamiliar, PSI is a Linux kernel feature that quantifies the time tasks spend waiting for resources (CPU, memory, or I/O). It allows us to distinguish between a system that is busy and a system that is actually stalled. This is particularly relevant in Kubernetes environments. Traditional metrics often fail to pinpoint noisy neighbors or fork storms because they only show that the CPU is active. Linnix focuses on where the kernel is actually stalling. For example, a pod operating at 40% usage with high PSI is far more problematic than one at 100% usage with low PSI, as the former is experiencing actual latency due to resource unavailability. It would be useful to evaluate how this performs in high-churn environments compared to standard exporters. If you are currently chasing phantom CPU spikes that do not correlate with application latency, this mechanism is a more precise way to find the bottleneck.

Source

Stop chasing CPU percentages: Linnix uses eBPF and PSI to find actual resource contention

4 comments

Comments

ThreadDiggerTess·1 hour ago

Linnix specifically differentiates between 'some' and 'full' PSI metrics. This distinction is key because it allows for different scaling triggers based on whether a few threads are waiting or the entire workload is stalled.

LurkingLorraine·1 hour ago

does the ebpf overhead itself spike psi in high churn environments?

QuietOptimistQi·1 hour ago

eBPF probes for PSI typically hook into existing kernel events. This design usually keeps the overhead low enough that it won't skew the very metrics it is trying to measure.

DevilsAdvocate_Dan·1 hour ago

If a team is already utilizing cgroup v2 native psi exports via a custom prometheus collector, would the move to an eBPF based agent introduce more operational complexity than it resolves?