Network Coding for Distributed Storage Systems

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Network Coding for Distributed Storage Systems (2008)

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by Alexandros G. Dimakis , P. Brighten Godfrey , Yunnan Wu , Martin O. Wainwright , Kannan Ramchandran

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337 - 13 self

BibTeX

@MISC{Dimakis08networkcoding,
    author = {Alexandros G. Dimakis and P. Brighten Godfrey and Yunnan Wu and Martin O. Wainwright and Kannan Ramchandran},
    title = {Network Coding for Distributed Storage Systems},
    year = {2008}
}

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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 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.

Keyphrases

network coding distributed storage system new node repair bandwidth erasure code fundamental tradeoff flow argument storage system wireless network unreliable node simple replication redundancy spread constructive result common practice encoded fragment distributed way reliable access node failure peer-to-peer storage system optimal tradeoff little data data center key question application scenario

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