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.

翻译：现代存储系统在处理读取请求序列时广泛采用数据预取算法。预取算法的性能（例如缓存系统命中率CHR的提升）直接影响存储系统的整体性能特征（读取延迟、IOPS等）。目前存在多种广为人知的预取算法，主要专注于发现请求流中的顺序模式。本研究探讨了一类专注于挖掘读取请求间伪随机（稀疏）模式的预取算法——稀疏预取算法。本文的核心贡献在于发现了一种新颖、轻量级的基于距离的稀疏预取算法族（DBSP），其在MSR轨迹数据集上的表现超越了先前已知的最佳结果。本文的另一重要贡献是对稀疏预取算法性能比较流程的详尽阐述。