Network Coding for Distributed Storage Systems

TL;DR

This paper introduces regenerating codes for distributed storage systems, enabling efficient data repair by reducing bandwidth and balancing storage costs, supported by theoretical analysis and network coding constructions.

Contribution

It proposes the concept of regenerating codes, characterizes the fundamental tradeoff between storage and repair bandwidth, and provides explicit code constructions achieving optimal points.

Findings

Regenerating codes reduce repair bandwidth significantly.

A fundamental tradeoff between storage and repair bandwidth is established.

Constructive codes achieve optimal points on the tradeoff curve.

Abstract

Distributed storage systems provide reliable access to data through redundancy spread over individually unreliable nodes. Application scenarios include data centers, peer-to-peer storage systems, and storage in wireless networks. Storing data using an erasure code, in fragments spread across nodes, requires less redundancy than simple replication for the same level of reliability. However, since fragments must be periodically replaced as nodes fail, a key question is how to generate encoded fragments in a distributed way while transferring as little data as possible across the network. For an erasure coded system, a common practice to repair from a node failure is for a new node to download subsets of data stored at a number of surviving nodes, reconstruct a lost coded block using the downloaded data, and store it at the new node. We show that this procedure is sub-optimal. We introduce the notion of regenerating codes, which allow a new node to download \emph{functions} of the stored data from the surviving nodes. We show that regenerating codes can significantly reduce the repair bandwidth. Further, we show that there is a fundamental tradeoff between storage and repair bandwidth which we theoretically characterize using flow arguments on an appropriately constructed graph. By invoking constructive results in network coding, we introduce regenerating codes that can achieve any point in this optimal tradeoff.