PFC: Transparent Optimization of Existing Prefetching Strategies for Multi-Level Storage Systems

Abstract

The multi-level storage architecture has been widely adopted in servers and data centers. However, while prefetching has been shown as a crucial technique to exploit the sequentiality in accesses common for such systems and hide the increasing relative cost of disk I/O, existing multi-level storage studies have focused mostly on cache replacement strategies. In this paper, we show that prefetching algorithms designed for single-level systems may have their limitations magnified when applied to multi-level systems. Overly conservative prefetching will not be able to effectively use the lower-level cache space, while overly aggressive prefetching will be compounded across levels and generate large amounts of wasted prefetch. We take an innovative approach to this problem: rather than designing a new, multi-level prefetching algorithm, we developed prefetching-coordinator (PFC), a hierarchy-aware optimization applicable to any existing prefetching algorithms. PFC does not require any application hints, a priori knowledge on the application access pattern or the native prefetching algorithm, or modification to the I/O interface. Instead, it monitors the upper-level access patterns as well as the lower-level cache status, and dynamically adjusts the aggressiveness of the lower-level prefetching activities. We evaluated PFC with extensive simulation study using a verified multi-level storage simulator, an accurate disk simulator, and access traces with different access patterns. Our results indicate that PFC dynamically controls lower-level prefetching in reaction to multiple system and workload parameters, improving the overall system performance in all 96 test cases. Working with four well-known existing prefetching algorithms adopted in real systems, PFC obtains an improvement of up to 35% to the average request response time, with an average improvement of 14.6% over all cases.