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Explicit Constructions of MBR and MSR Codes for Clustered Distributed Storage

2018/01/08 by Jy-yong Sohn, Sohn, Jy-yong, Beongjun Choi +3
Computer Science · #Advanced Data Storage Technologies #Caching and Content Delivery #Cooperative Communication and Network Coding #FOS: Computer and information sciences #Information Theory (cs.IT)

paper · pdf · doi:10.48550/arxiv.1801.02287

openalex publication_date 2018/01/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper considers capacity-achieving coding for the clustered form of distributed storage that reflects practical storage networks. To reflect the clustered structure with limited cross-cluster communication bandwidths, nodes in the same cluster are set to communicate βI symbols, while nodes in other clusters can communicate βc ≤ βI symbols with one another. We provide two types of exact regenerating codes which achieve the capacity of clustered distributed storage: the minimum-bandwidth-regenerating (MBR) codes and the minimum-storage-regenerating (MSR) codes. First, we construct MBR codes for general parameter settings of clustered distributed storage. The suggested MBR code is a generalization of an existing code proposed by Rashmi et al., for scenarios where storage nodes are dispersed into L > 1 clusters. The proposed MBR code for the βc = 0 case requires a much smaller field size compared to existing local MBR codes. Secondly, we devise MSR codes for clustered distributed storage. Focus is given on two important cases:ε=0 and ε∈ [1/(n-k), 1], where ε=βcI is the ratio of the available cross- to intra-cluster repair bandwidths, n is the total number of distributed nodes and k is the number of contact nodes in data retrieval. The former represents the scenario where cross-cluster communication is not allowed, while the latter corresponds to the case of minimum node storage overhead. For ε=0, two existing locally repairable codes are proven to be MSR codes for the clustered model. For ε∈ [1/(n-k), 1], existing MSR codes for the non-clustered model are applicable to clustered scenarios with a simple modification.

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