HFR Code: A Flexible Replication Scheme for Cloud Storage Systems

TL;DR

This paper introduces heterogeneous fractional repetition (HFR) codes for cloud storage, enabling flexible, efficient, and capacity-achieving data repair with reduced reconstruction time through clustering.

Contribution

It generalizes FR codes to heterogeneous degrees, provides explicit constructions using group divisible designs, and proposes a clustering framework to optimize data reconstruction.

Findings

Codes achieve system storage capacity under random access repair.

HFR codes offer multiple repair options for node failures.

Clustering reduces data reconstruction time.

Abstract

Fractional repetition (FR) codes are a family of repair-efficient storage codes that provide exact and uncoded node repair at the minimum bandwidth regenerating point. The advantageous repair properties are achieved by a tailor-made two-layer encoding scheme which concatenates an outer maximum-distance-separable (MDS) code and an inner repetition code. In this paper, we generalize the application of FR codes and propose heterogeneous fractional repetition (HFR) code, which is adaptable to the scenario where the repetition degrees of coded packets are different. We provide explicit code constructions by utilizing group divisible designs, which allow the design of HFR codes over a large range of parameters. The constructed codes achieve the system storage capacity under random access repair and have multiple repair alternatives for node failures. Further, we take advantage of the systematic feature of MDS codes and present a novel design framework of HFR codes, in which storage nodes can be wisely partitioned into clusters such that data reconstruction time can be reduced when contacting nodes in the same cluster.