Harnessing Parallelism for Fast Data Repair in MSR-Coded Storage
Xiaolu Li, Han Yuan, Xuan Liu, J. H. Zhang, Patrick P. C. Lee, Yuchong Hu, Dan Feng · ACM Transactions on Storage · 2025
Minimum-storage regenerating (MSR) codes are provably optimal erasure codes that minimize the repair bandwidth (i.e., the amount of traffic being transferred during a repair operation), while minimizing storage redundancy, in distributed storage systems. However, the practical repair performance of MSR codes still has significant room for improvements, as their mathematical structure makes repair operations difficult to parallelize. In this article, we present HyperParaRC, a parallel repair framework for MSR codes. HyperParaRC leverages the sub-packetization nature of MSR codes to parallelize the repair of sub-blocks and balance repair load (i.e., the amount of traffic sent or received by a node) across available nodes. We first demonstrate that there exists a trade-off between repair bandwidth and maximum repair load. We then propose an affinity-based heuristic for HyperParaRC, which approximately minimizes the maximum repair load by examining the bandwidth incurred during sub-block computations and significantly reduces the search time for large coding parameters compared with our earlier work, ParaRC. Based on our affinity-based heuristic, we further design a full-node recovery mechanism for HyperParaRC that combines both intra-stripe and inter-stripe parallel repair scheduling to repair multiple lost blocks in a failed node. We prototype HyperParaRC on Hadoop HDFS and evaluate it on Alibaba Cloud. Our evaluation results show that HyperParaRC reduces both single-block repair and full-node recovery times compared with state-of-the-art repair approaches.