A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit <a rel="external noopener" href="https://arxiv.org/pdf/1705.07400v1.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
<span class="release-stage" >pre-print</span>
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<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1705.07400v1">arXiv:1705.07400v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zoj7chgrore7poh62jqfeqlrwu">fatcat:zoj7chgrore7poh62jqfeqlrwu</a> </span>
more »... ly 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.
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