Repair-Optimal Data Placement for Locally Repairable Codes with Optimal Minimum Hamming Distance
Shuang Ma, Si Wu, Cheng Li, Yinlong Xu · 2022
Modern clustered storage systems increasingly adopt erasure coding to realize reliable data storage at low storage redundancy. Locally Repairable Codes (LRC) are a family of practical erasure codes with high repair efficiency. Among various LRC constructions, Optimal-LRC is a recently proposed LRC approach that achieves the optimal Minimum Hamming Distance with low theoretical repair costs. In this paper, we consider the repair performance of Optimal-LRC in clustered storage systems. We show that the conventional flat data placement and random data placement incur substantial cross-cluster repair traffic, which impairs the repair performance. To this end, we design an optimal data placement scheme that provably minimizes the cross-cluster repair traffic, by carefully placing each group of blocks in Optimal-LRC into a minimum number of clusters subject to single-cluster fault tolerance. We implement our optimal data placement scheme on a key-value store prototype atop Memcached, and show via LAN testbed experiments that the optimal data placement significantly improves the repair performance compared to the conventional data placements.