Title: MITHRIL: Mining Sporadic Associations for Cache Prefetching,
Abstract: 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.