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

机译:在本地可修复代码中最佳修复缩放权衡:分析和评估

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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.
机译:如何提高擦除编码存储的修复性能是维持现代大规模存储系统高可靠性的关键问题。 本地可修复的代码(LRC)是一种流行的维修效率擦除代码系列,可在实践中进行修复带宽,并在实践中部署。 为了适应访问效率和容错的需求不断变化,现代存储系统还在擦除擦除数据上进行频繁的缩放操作。 在本文中,我们分析了集群存储系统中LRC的修复和缩放性能之间的最佳权衡。 具体而言,我们专注于两个最佳的修复缩放权衡,以及沿着两种最佳修复缩放权衡曲线运行的设计放置策略,受到容错约束的影响。 我们的原型和评估我们在LAN测试的位置策略,并表明它们优于修复和缩放操作中的传统放置方案。

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