Distance based prefetching algorithms for mining of the sporadic requests associations

TL;DR

This paper introduces a new family of lightweight distance-based sporadic prefetching algorithms that improve cache hit ratios by effectively mining pseudo-random request patterns, outperforming previous methods on MSR traces.

Contribution

It presents a novel distance-based sporadic prefetching family (DBSP) that outperforms existing algorithms and provides a comprehensive performance comparison procedure.

Findings

DBSP algorithms outperform previous sporadic prefetchers on MSR traces

The new algorithms increase cache hit ratios in storage systems

Thorough performance comparison methodology is proposed

Abstract

Modern storage systems intensively utilize data prefetching algorithms while processing sequences of the read requests. Performance of the prefetching algorithm (for instance increase of the cache hit ratio of the cache system - CHR) directly affects overall performance characteristics of the storage system (read latency, IOPS, etc.). There are widely known prefetching algorithms that are focused on the discovery of the sequential patterns in the stream of requests. This study examines a family of prefetching algorithms that is focused on mining of the pseudo random (sporadic) patterns between read requests - sporadic prefetching algorithms. The key contribution of this paper is that it discovers a new, lightweight family of distance-based sporadic prefetching algorithms (DBSP) that outperforms the best previously known results on MSR traces collection.Another important contribution of this paper is a thorough description of the procedure for comparing the performance of sporadic prefetchers.