Prefetching is an effective technique for improving file access performance, which can reduce access latency for I/O systems. In distributed storage system, prefetching for metadata files is critical for the overall system performance. In this paper, an Affinity-based Metadata Prefetching (APM) scheme is proposed for metadata servers in large-scale distributed storage systems to provide aggressive metadata prefetching. Through mining useful information about metadata assesses from past history, AMP can discover metadata file affinities accurately and intelligently for prefetching. Compared with LRU and some of the latest file prefetching algorithms such as NEXUS and C-miner, trace-driven simulations show that AMP can improve the hit rates by up to 12%, 4.5% and 4%, respectively, while reduce the average response time by up to 60%, 12% and 8%, respectively.
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