LumOS: LLM Agents for Always‑On OS Tuning

Overview

LumOS builds an expert‑in‑residence agent that safely tunes Linux kernel schedulers online. The agent reasons over live telemetry, proposes settings, and applies changes with guards (transactional apply/commit/revert, approvals), delivering faster convergence and lower tail latency than classical tuners or manual tuning, and adapting quickly to workload shifts.

Why it matters

Traditional BO/RL tuners often explore blindly, need brittle reward engineering, and adapt slowly. By emulating human expert reasoning, the agent interprets system state, chooses safe steps, and explains what it’s doing—making OS auto‑tuning governable and auditable in production environments.

What’s new here

• LLM‑guided control loop: An LLM proposes scheduler settings (e.g., min_granularity_ns, latency_ns) from structured telemetry and trend summaries; host‑side guardrails ensure safety (typed tools, policy, approval gates, apply/commit/revert).

• Zero‑mod deployment path: A userspace agent learns from existing counters (perf, /proc, /sys)—no app changes and no kernel patches. It avoids brittle single‑metric proxies by using pairwise ranking and archetype selection for robust decisions across contexts.

• Latency‑aware speculation (optional): A fast speculator makes immediate, reversible OS adjustments while the slower actor deliberates, improving reaction time without sacrificing correctness (last‑write‑wins).

Results at a glance

• Beats classical tuners & human expert on Linux CFS tuning
  – −5.0% p99 vs. Bayesian (1‑param), −7.1% vs. Bayesian (2‑param), and −2.98% vs. a kernel‑savvy human overall; converges faster and adapts smoothly to rate changes.
• Robust with zero app instrumentation
  – Single‑metric proxies (e.g., IPC) are brittle across workloads; pairwise ranking over diversified counters stays predictive even under noise and antagonists.
• Faster reaction via speculation
  – During recovery, p95 latency ~37.9 ms with actor+speculator vs. 54.0 ms actor‑only (untuned ~103 ms); convergence in ~10–15 s vs. ~200 s for actor‑only.

How it works

1. Observe: Collect a compact performance signature from system counters (CPU/core, scheduler, memory/VM, LLC, I/O, network).
2. Reason:
   • LLM expert: summarizes trends, proposes safe next settings; guardrails (MCP‑style tool schemas, policy, approvals) keep actions auditable and reversible.
   • Zero‑mod ML: if app metrics are absent, a pairwise ranker selects better configs based on the signature; archetype gating picks a specialized ranker or falls back to a global one.
3. Act: Apply staged changes, measure, and commit/rollback; optionally let a fast speculator react every second while the actor finalizes.

Safety & governance

We adopt MCP‑style interfaces: discoverable, typed tools; semantic validation (units/ranges/cross‑field checks); two‑phase apply–commit–revert; policy/approval gates; and structured audit logs for forensics and replay.

Broader thrusts

Beyond OS control, we generalize speculative actions to agentic environments (gameplay, e‑commerce, web QA): a fast model predicts likely next steps while authoritative components verify. This lossless pattern yields substantial wall‑clock savings—up to ~30% time reduction in end‑to‑end agent runs under representative settings—and integrates cleanly with tool- and human‑in‑the‑loop pipelines.