A Survey on Network Codes for Distributed Storage

By A. Dimakis, Kannan Ramchandran, Yunnan Wu, Changho Suh. Proceedings of the IEEE, 2010

716 citations recorded by the source corpus.

Distributed storage systems often introduce redundancy to increase reliability. When coding is used, the repair problem arises: if a node storing encoded information fails, in order to maintain the same level of reliability we need to create encoded information at a new node. This amounts to a partial recovery of the code, whereas conventional erasure coding focuses on the complete recovery of the information from a subset of encoded packets. The consideration of the repair network traffic gives rise to new design challenges. Recently, network coding techniques have been instrumental in addressing these challenges, establishing that maintenance bandwidth can be reduced by orders of magnitude compared to standard erasure codes. This paper provides an overview of the research results on this topic.

Subfields: Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/f8ada311194a2e48796d88252d90aada5f81c1cf

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In Computer Networks and Communications

Citations in the corpus

Fractional Repetition and Erasure Batch Codes (2014, background): “optimize the storage overhead and repair bandwidth, respectively (for constructions see [4, 5, 12, 13] and references therein).”
A Survey on the Edge Computing for the Internet of Things (2018, background): “There are several available measurement schemes [82]–[84] proposed to handle this issue.”
The Capacity of Robust Private Information Retrieval With Colluding Databases (2016, background): “2777490 connections between locally decodable and locally recoverable codes [12], PIR also connects to distributed data storage repair [13] and index coding [14], which in turn encompass all of network coding [15].”
Communication Efficient Secret Sharing (2015, background): “Besides its application in distributed storage of secret data, secret sharing became a fundamental cryptographic primitive and is used as a building block in numerous secure protocols [1].”
The Capacity of Private Information Retrieval (2016, background): “Through the connection between locally decodable and locally recoverable codes [20], PIR also connects to distributed data storage repair [21], index coding [2] and the entire umbrella of network coding [22] in general.”
Topological Interference Management Through Index Coding (2013, background): “Conversely, network coding applications such as distributed storage repair [39], caching [40], etc.”
Wireless Network Virtualization: A Survey, Some Research Issues and Challenges (2015, background): “, virtual memory [1], virtual machines [2], virtual storage access network [3] and virtual data centers [4].”
Batch codes through dense graphs without short cycles (2014, background): “ erasure codes provide fault tolerance for a single large object split in multiple symbols. One issue that blurs this difference, however, is the so-called repair problem of distributed storage codes [10, 11]. When a systematic code is used for storage across servers a very common operation is the a”
The Capacity of Private Information Retrieval From Coded Databases (2016, background): “Due to node failures and erasures that arise naturally in any storage system, redundancy should be introduced [12].”