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Reducing File System Tail Latencies with Chopper
Jun He, Duy Nguyen, Andrea C. Arpaci-Dusseau, and Remzi H. Arpaci-Dusseau, University of Wisconsin—Madison
We present Chopper, a tool that efficiently explores the vast input space of file system policies to find behaviors that lead to costly performance problems. We focus specifically on block allocation, as unexpected poor layouts can lead to high tail latencies. Our approach utilizes sophisticated statistical methodologies, based on Latin Hypercube Sampling (LHS) and sensitivity analysis, to explore the search space efficiently and diagnose intricate design problems. We apply Chopper to study the overall behavior of two file systems, and to study Linux ext4 in depth. We identify four internal design issues in the block allocator of ext4 which form a large tail in the distribution of layout quality. By removing the underlying problems in the code, we cut the size of the tail by an order of magnitude, producing consistent and satisfactory file layouts that reduce data access latencies.
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