11 papers
AtumAI: A Principled Framework for Agentic Generation of Datacenter Control-Plane Policies
Qiushi Lin, Chaojie Zhang, Ãñigo Goiri +3
The efficiency of a datacenter rests on its control plane policies. Designing these policies is increasingly hard: the hardware-software stack grows fast, the design space is vast…
Vulcan: Instance-specialized, Verifiable Systems Heuristics Through LLM-driven Search
Rohit Dwivedula, Divyanshu Saxena, Sujay Yadalam +3
Systems resource management tasks rely primarily on hand-designed heuristics. However, growing hardware heterogeneity and workload diversity require heuristics specialized to parti…
AutoScout: Structured Optimization for Automating ML System Configuration
Jimmy Shong, Yuhan Ding, Yihan Jiang +5
Machine learning (ML) systems expose a rapidly expanding configuration space spanning model-parallelism strategies, communication optimizations, and low-level runtime parameters. E…
Canopy: Property-Driven Learning for Congestion Control
Chenxi Yang, Divyanshu Saxena, Rohit Dwivedula +3
Learning-based congestion controllers offer better adaptability compared to traditional heuristics. However, the unreliability of learning techniques can cause learning-based contr…
Continuous Benchmark Generation for Evaluating Enterprise-scale LLM Agents
Divyanshu Saxena, Rishikesh Maurya, Xiaoxuan Ou +7
The rapid adoption of AI agents across domains has made systematic evaluation crucial for ensuring their usefulness and successful production deployment. Evaluation of AI agents ty…
A Joint Learning Approach to Hardware Caching and Prefetching
Samuel Yuan, Divyanshu Saxena, Jiayi Chen +2
Several learned policies have been proposed to replace heuristics for scheduling, caching, and other system components in modern systems. By leveraging diverse features, learning f…