SARC: Sequential Prefetching in Adaptive Replacement Cache

Sequentiality of reference is an ubiquitous access pattern dating back at least to Multics. Sequential workloads lend themselves to highly accurate prediction and prefetching. In spite of the simplicity of the workload, design and analysis of a good sequential prefetching algorithm and associated cache replacement policy turns out to be surprisingly intricate. As first contribution, we uncover and remedy an anomaly (akin to famous Belady's anomaly) that plagues sequential prefetching when integrated with caching. Typical workloads contain a mix of sequential and random streams. As second contribution, we design a self-tuning, low overhead, simple to implement, locally adaptive, novel cache management policy SARC that dynamically and adaptively partitions the cache space amongst sequential and random streams so as to reduce the read misses. As third contribution, we implemented SARC along with two popular state-of-theart LRU variants on hardware for IBM's flagship storage controller Shark. On Shark hardware with 8 GB cache and 16 RAID-5 arrays that is serving a workload akin to Storage Performance Council's widely adopted SPC-1 benchmark, SARC consistently and dramatically outperforms the two LRU variants shifting the throughput-response time curve to the right and thus fundamentally increasing the capacity of the system. As anecdotal evidence, at the peak throughput, SARC has average response time of 5.18ms as compared to 33.35ms and 8.92ms for the two LRU variants.