FSA: Benchmarking Fail-Slow Algorithms
exploring LLM in the RAID slowdown condition analysis
Hi everyone! I’m Xikang, a master’s CS student at UChicago. As a part of FSA benchmarking Project, I’m thrilled to be a contributor to OSRE 2024, collaborating with Kexin Pei, the assistant Professor of Computer Science at Uchicago and Ruidan, a talented PhD student at UChicago.
This summer, I will focus on integrating some advanced ML into our RAID slowdown analysis. Our aim is to assess whether LLMs can effectively identify RAID slowdown issues and to benchmark their performance against our current machine learning algorithms. We will test the algorithms on Chameleon Cloud and benchmark them.
Additionally, we will explore optimization techniques to enhance our pipeline and improve response quality. We hope this research will be a start point for future work, ultilizing LLMs to overcome the limitations of existing algorithms and provide a comprehensive analysis that enhances RAID and other storage system performance.
I’m excited to work with all of you and look forward to your suggestions. if you are interested, Here is my proposal
Kexin Pei
Neubauer Family Assistant Professor of Computer Science, University of Chicago
Kexin is the Neubauer Family Assistant Professor of Computer Science. His research lies at the intersection of Security, Software Engineering, and Machine Learning, focusing on developing data-driven program analysis approaches to improve the security and reliability of traditional and AI-based software systems.
Ruidan Li
Ph.D. Student, University of Chicago
Ph.D. student at the Department of Computer Science at University of Chicago, advised by Prof. Haryadi S. Gunawi.
Xikang Song
Master’s student at University of Chicago
Xikang Song is a Master’s student at the University of Chicago, majoring in Computer Science with a interest in computer systems and machine learning.