In many of today's applications, access to storage constitutes the major cost of processing a user request. Data prefetching has been used to alleviate the storage access latency. Under current prefetching techniques, the storage system prefetches a batch of blocks upon detecting an access pattern. However, the high level of concurrency in today's applications typically leads to interleaved block accesses, which makes detecting an access pattern a very challenging problem. Towards this, we propose and evaluate QuickMine, a novel, lightweight and minimally intrusive method for context-aware prefetching. Under QuickMine, we capture application contexts, such as a transaction or query, and leverage them for context-aware prediction and improved prefetching effectiveness in the storage cache. We implement a prototype of our context-aware prefetching algorithm in a storage-area network (SAN) built using Network Block Device (NBD). Our prototype shows that context-aware prefetching clearly outperforms existing context-oblivious prefetching algorithms, resulting in factors of up to 2 improvements in application latency for two e-commerce workloads with repeatable access patterns, TPC-W and RUBiS.
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