Fast Proactive Repair in Erasure-Coded Storage: Analysis, Design, and Implementation

Erasure coding offers a storage-efficient redundancy mechanism for maintaining data availability guarantees in large-scale storage clusters, yet it also incurs high performance overhead in failure repair. Recent developments in accurate disk failure prediction allow soon-to-fail (STF) nodes to be repaired in advance, thereby opening new opportunities for accelerating failure repair in erasure-coded storage. To this end, we present a fast proactive repair solution called FastPR, which carefully couples two repair methods, namely migration (i.e., relocating the chunks of an STF node) and reconstruction (i.e., decoding the chunks of an STF node through erasure coding), so as to fully parallelize the repair operation across the storage cluster. FastPR solves a bipartite maximum matching problem and schedules both migration and reconstruction in a parallel fashion. We show that FastPR significantly reduces the repair time over the baseline repair approaches for both Reed-Solomon codes and Azure's Local Reconstruction Codes via mathematical analysis, large-scale simulation, and Amazon EC2 experiments.

Automated summary

Erasure coding provides a storage-efficient redundancy mechanism for large-scale storage clusters, but it incurs high performance overhead in failure repair. Recent developments in accurate disk failure prediction allow repairing soon-to-fail nodes in advance, opening new opportunities for accelerating failure repair in erasure-coded storage. FastPR is a fast proactive repair solution that fully parallelizes the repair operation by coupling migration and reconstruction methods. FastPR significantly reduces repair time for both Reed-Solomon codes and Azure's Local Reconstruction Codes.