Optimal Repair-Scaling Trade-off in Locally Repairable Codes: Analysis and Evaluation

Abstract

How to improve the repair performance of erasure-coded storage is a critical issue for maintaining high reliability of modern large-scale storage systems. Locally repairable codes (LRC) are one popular family of repair-efficient erasure codes that mitigate the repair bandwidth and are deployed in practice. To adapt to the changing demands of access efficiency and fault tolerance, modern storage systems also conduct frequent scaling operations on erasure-coded data. In this article, we analyze the optimal trade-off between the repair and scaling performance of LRC in clustered storage systems. Specifically, we focus on two optimal repair-scaling trade-offs, and design placement strategies that operate along the two optimal repair-scaling trade-off curves subject to the fault tolerance constraints. We prototype and evaluate our placement strategies on a LAN testbed, and show that they outperform the conventional placement schemes in repair and scaling operations.