7 papers · 1 filter
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…
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…
Man-Made Heuristics Are Dead. Long Live Code Generators!
Rohit Dwivedula, Divyanshu Saxena, Aditya Akella +2
Policy design for various systems controllers has conventionally been a manual process, with domain experts carefully tailoring heuristics for the specific instance in which the po…
ConfigBot: Adaptive Resource Allocation for Robot Applications in Dynamic Environments
Rohit Dwivedula, Sadanand Modak, Aditya Akella +3
The growing use of service robots in dynamic environments requires flexible management of on-board compute resources to optimize the performance of diverse tasks such as navigation…
Large Language Models as Realistic Microservice Trace Generators
Donghyun Kim, Sriram Ravula, Taemin Ha +3
Workload traces are essential to understand complex computer systems' behavior and manage processing and memory resources. Since real-world traces are hard to obtain, synthetic tra…
Reforge: Low-Latency Distributed GNN Serving with Selective Embedding Recomputation
Geon-Woo Kim, Donghyun Kim, Jeongyoon Moon +5
Graph Neural Networks (GNNs) have been widely adopted for their ability to compute expressive node representations in graph datasets. However, serving GNNs on large graphs is chall…