InfraGym: Empowering LLM Agents for Real-World Computer System Optimization

Huaizheng Zhang, Lei Zhang, Yuanming Li, Yizheng Huang, Xiaotong Yang, Kuntai Du, Yihua Cheng, Junchen Jiang, Wencong Xiao

Keywords: LLM; Agent; Computer Systems; Infrastructure

TL;DR: InfraGym, an open, extensible platform where researchers can study computer system optimization with LLM agents

Abstract:

Large language model (LLM) agents have demonstrated high potential of improving performance for complex computer system, such as cluster scheduling, network congestion control, and adaptive video streaming. However, in lack of a standard, safe, and extensible benchmarking platform, it is difficult to evaluate whether these LLM agents improve real-world system performance and by how much.
We present InfraGym, an open, extensible platform where researchers can study computer system optimization with LLM agents.
Our current release includes three real-world cases and supports interaction with both simulated and real environments. We benchmark multiple LLM agents on these tasks using both open-source and closed-source LLMs, and outline future directions. The code is available at \hyperlink{https://github.com/MLSysOps/InfraGym}{https://github.com/MLSysOps/InfraGym}

Submission Number: 5