FetchPipe: Data Science Pipeline for ML-based Prefetching

Hello, I’m Peiran Qin, a first-year Pre-Doctoral student in Computer Science at the University of Chicago. In this summer I will focus working on the project FetchPipe: Data Science Pipeline for ML-based Prefetching under the mentorship of Prof. Haryadi S. Gunawi. This is my proposal.

Caching and prefetching are integral components of modern storage systems, aimed at reducing I/O latency by utilizing faster but less dense memory for storing data that is accessed frequently. Traditional prefetching strategies, which primarily rely on heuristic-based methods, often fall short in performance, particularly in complex scenarios. To address the complex scenarios, in recent years, machine learning solutions have emerged as a promising alternative, offering the ability to learn and predict complicated data access patterns. However, each existing ML prefetcher may bias toward different scenarios and distinct evaluation metrics. There is still a necessity to evaluate state-of-the-art machine learning based literatures comprehensively and fairly under an aligned evaluation framework and extensive performance metrics. Therefore, It becomes the motivation for me to spend my summer on this interesting project!