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Due to individual unreliable commodity components, failures are common inlarge-scale distributed storage systems. Erasure codes are widely deployed inpractical storage systems to provide fault tolerance with low storage overhead.However, random data distribution (RDD), commonly used in erasure-coded storagesystems, induces heavy cross-rack traffic, load imbalance, and random access,which adversely affects failure recovery. In this paper, with orthogonalarrays, we define a Deterministic Data Distribution ($D^3$) to uniformlydistribute data/parity blocks among nodes, and propose an efficient failurerecovery approach based on $D^3$, which minimizes the cross-rack repair trafficagainst a single node failure. Thanks to the uniformity of $D^3$, the proposedrecovery approach balances the repair traffic not only among nodes within arack but also among racks. We implement $D^3$ over Reed-Solomon codes andLocally Repairable Codes in Hadoop Distributed File System (HDFS) with acluster of 28 machines. Compared with RDD, our experiments show that $D^3$significantly speeds up the failure recovery up to 2.49 times for RS codes and1.38 times for LRCs. Moreover, $D^3$ supports front-end applications betterthan RDD in both of normal and recovery states.

Deterministic Data Distribution for Efficient Recovery in Erasure-Coded Storage Systems