2017/05/21 by Juncheng Yang, Reza Karimi, Yang, Juncheng +8
Computer Science · #Advanced Data Storage Technologies #Caching and Content Delivery #Cloud Computing and Resource Management #Distributed #FOS: Computer and information sciences #Operating Systems (cs.OS) #Parallel #Performance (cs.PF) #and Cluster Computing (cs.DC) #cs.DC #cs.OS #cs.PF
paper · pdf · doi:10.48550/arxiv.1705.07400
arxiv created 2017/05/21 · openalex publication_date 2017/05/21 · arxiv updated 2017/05/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The growing pressure on cloud application scalability has accentuated storage performance as a critical bottle- neck. Although cache replacement algorithms have been extensively studied, cache prefetching - reducing latency by retrieving items before they are actually requested remains an underexplored area. Existing approaches to history-based prefetching, in particular, provide too few benefits for real systems for the resources they cost. We propose MITHRIL, a prefetching layer that efficiently exploits historical patterns in cache request associations. MITHRIL is inspired by sporadic association rule mining and only relies on the timestamps of requests. Through evaluation of 135 block-storage traces, we show that MITHRIL is effective, giving an average of a 55% hit ratio increase over LRU and PROBABILITY GRAPH, a 36% hit ratio gain over AMP at reasonable cost. We further show that MITHRIL can supplement any cache replacement algorithm and be readily integrated into existing systems. Furthermore, we demonstrate the improvement comes from MITHRIL being able to capture mid-frequency blocks.