Yanpeng Hu

Yusheng Zheng, Yanpeng Hu, Wei Zhang et al.

Operating system schedulers suffer from a fundamental semantic gap, where kernel policies fail to understand application-specific needs, leading to suboptimal performance. We introduce SchedCP, the first framework that enables fully autonomous Large Language Model (LLM) agents to safely and efficiently optimize Linux schedulers without human involvement. Our core insight is that the challenge is not merely to apply a better LLM, but to architect a decoupled control plane that separates the AI's role of semantic reasoning ("what to optimize") from the system's role of execution ("how to observe and act"), thereby separating the optimization problem into two stages: goal-inference and policy-synthesis. Implemented as Model Context Protocol(MCP) server, SchedCP provides a stable interface with three key services: a Workload Analysis Engine, an evolving Scheduler Policy Repository, and an Execution Verifier that validates all AI-generated code and configure before deployment with static and dynamic analysis. We demonstrate this architecture's power with sched-agent, a multi-agent system that autonomously analyzes workloads, synthesizes custom eBPF scheduling policies, and deploys them via the sched\_ext infrastructure. Our evaluation shows that SchedCP achieves up to an 1.79x performance improvement, and a 13x cost reduction compared to naive agentic approaches, all while maintaining high success rate. By bridging the semantic gap, SchedCP democratizes expert-level system optimization and represents a step towards creating truly self-optimizing, application-aware operating systems. The code is open-sourced in https://github.com/eunomia-bpf/schedcp

Yiwei Yang, Yanpeng Hu, Yusheng Zheng et al.

The widening gap between processor speed and storage latency has made data movement a dominant bottleneck in modern systems. Two lines of storage-layer innovation attempted to close this gap: persistent memory shortened the latency hierarchy, while computational storage devices pushed processing toward the data. Neither has displaced conventional NVMe SSDs at scale, largely due to programming complexity, ecosystem fragmentation, and thermal/power cliffs under sustained load. We argue that storage-side compute should be \emph{reversible}: computation should migrate dynamically between host and device based on runtime conditions. We present \sys, which realizes this principle on CXL SSDs by decomposing I/O-path logic into migratable \emph{storage actors} compiled to WebAssembly. Actors share state through coherent CXL.mem regions; an agility-aware scheduler migrates them via a zero-copy drain-and-switch protocol when thermal or power constraints arise. Our evaluation on an FPGA-based CXL SSD prototype and two production CSDs shows that \sys turns hard thermal cliffs into elastic trade-offs, achieving up to 2$\times$ throughput improvement and 3.75$\times$ write latency reduction without application modification.

Yimin Tang, Tian Jiang, Yanpeng Hu

Minesweeper as a puzzle video game and is proved that it is an NPC problem. We use CSP, Logic Inference and Sampling to make a minesweeper solver and we limit us each select in 5 seconds.