TuxBot: Semantic-Aware Online OS Tuning with LLMs

Online OS tuning can improve long-running services, but existing tuners are not well suited for live hosts. They treat scheduler, power, memory, and I/O controls as black-box variables and optimize a scalar reward. This approach ignores cross-knob policy structure, breaks down when application metrics are unavailable, and can push a running service into degraded regions that persist after a bad setting is removed. We present TuxBot, a bounded LLM-based tuner for steady-state online OS tuning. TuxBot constructs a structured decision context from telemetry, knob schemas, recent trajectories, and prior sessions; pairs low-latency and deeper reasoning models; and applies only typed, validated knob updates. TuxBot is able to reason about knob semantics, subsystem interactions, and indirect performance signals without granting the model unconstrained host control.