A code symbol of a linear code is said to have locality r if this symbol could be recovered by at most r other code symbols. An (n,k,r) locally repairable code (LRC) with all symbol locality is a linear code with length n, dimension k, and locality r for all symbols. Recently, there are lots of studies on the bounds and constructions of LRCs, most of which are essentially based on the generator matrix of the linear code. Up to now, the most important bounds of minimum distance for LRCs might be the well-known Singleton-like bound and the Cadambe-Mazumdar bound concerning the field size. In this paper, we study the bounds and constructions of LRCs from views of parity-check matrices. Firstly, we set up a new characterization of the parity-check matrix for an LRC. Then, the proposed parity-check matrix is employed to analyze the minimum distance. We give an alternative simple proof of the well-known Singleton-like bound for LRCs with all symbol locality, and then easily generalize it to a more general bound, which essentially coincides with the Cadambe-Mazumdar bound and includes the Singleton-like bound as a specific case. Based on the proposed characterization of parity-check matrices, necessary conditions of meeting the Singleton-like bound are obtained, which naturally lead to a construction framework of good LRCs. Finally, two classes of optimal LRCs based on linearized polynomial theories and Vandermonde matrices are obtained under the construction framework.
These codes @cite_2 are a class of MDS codes with subpacketization. When a single node failure occures, the repair process involves more than @math nodes and each node transfers a linear combination of the packets it stores, which reduces bandwidths compared to classical MDS codes. See @cite_22 @cite_17 for the construction of regenerating codes and @cite_29 for a survey.
@cite_29 @cite_17 @cite_22 @cite_2
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This paper addresses the problem of constructing maximum distance separable (MDS) codes that enable exact reconstruction (repair) of each code block by downloading a small amount of information from the remaining code blocks. The total amount of information flow from the remaining code blocks during this reconstruction process is referred to as repair bandwidth of the underlying code. Existing constructions of exact-repairable MDS codes with optimal repair bandwidth require working with large subpacketization levels, which restrict their applicability in practice. This paper presents two general approaches to construct exact-repairable MDS codes that aim at significantly reducing the required subpacketization level at the cost of slightly suboptimal repair bandwidth. The first approach provides MDS codes that have repair bandwidth at most twice the optimal repair bandwidth. In addition, these codes also have the smallest possible subpacketization level @math , where @math denotes the number of parity blocks. This approach is then generalized to design codes that have their repair bandwidth approaching the optimal repair bandwidth at the cost of graceful increment in the required subpacketization level. The second approach transforms an MDS code with optimal repair bandwidth and large subpacketization level into a longer MDS code with small subpacketization level and near-optimal repair bandwidth. For a given @math , the codes constructed using this approach have their subpacketization level scaling logarithmically with the code length. In addition, the obtained codes require field size only linear in the code length and ensure load balancing among the intact code blocks in terms of the information downloaded from these blocks during the exact reconstruction of a code block. We propose a generic transformation that can convert any nonbinary @math maximum distance separable (MDS) code into another @math MDS code over the same field such that: 1) some arbitrarily chosen @math nodes have the optimal repair bandwidth and the optimal rebuilding access; 2) for the remaining @math nodes, the normalized repair bandwidth and the normalized rebuilding access (over the file size) are preserved; and 3) the sub-packetization level is increased only by a factor of @math . Two immediate applications of this generic transformation are then presented. The first application is that we can transform any nonbinary MDS code with the optimal repair bandwidth or the optimal rebuilding access for the systematic nodes only, into a new MDS code which possesses the corresponding repair optimality for all nodes. The second application is that by applying the transformation multiple times, any nonbinary @math scalar MDS code can be converted into an @math MDS code with the optimal repair bandwidth and the optimal rebuilding access for all nodes, or only a subset of nodes, whose sub-packetization level is also optimal. The high repair bandwidth cost of (n,k) maximum distance separable (MDS) erasure codes has motivated a new class of codes that can reduce repair bandwidth over that of conventional MDS codes. In this paper, we address (n,k,d) exact repair MDS codes, which allow for any single failed node to be repaired exactly with access to any arbitrary set of d survivor nodes. We show the existence of exact repair MDS codes that achieve minimum repair bandwidth (matching the cut-set lower bound) for arbitrary admissible (n,k,d), i.e., k ≤ d ≤ n-1. Moreover, we extend our results to show the optimality of our codes for multiple-node failure scenarios in which an arbitrary set of r ≤ n-k failed nodes needs to repaired. Our approach is based on asymptotic interference alignment proposed by Cadambe and Jafar. As a byproduct, we also characterize the capacity of a class of multisource nonmulticast networks. Several works have developed vector-linear maximum-distance separable (MDS) storage codes that minimize the total communication cost required to repair a single coded symbol after an erasure, referred to as repair bandwidth (BW). Vector codes allow communicating fewer sub-symbols per node, instead of the entire content. This allows non trivial savings in repair BW. In sharp contrast, classic codes, like Reed-Solomon (RS), used in current storage systems, are deemed to suffer from naive repair, i.e. downloading the entire stored message to repair one failed node. This mainly happens because they are scalar-linear. In this work, we present a simple framework that treats scalar codes as vector-linear. In some cases, this allows significant savings in repair BW. We show that vectorized scalar codes exhibit properties that simplify the design of repair schemes. Our framework can be seen as a finite field analogue of real interference alignment. Using our simplified framework, we design a scheme that we call clique-repair which provably identifies the best linear repair strategy for any scalar 2-parity MDS code, under some conditions on the sub-field chosen for vectorization. We specify optimal repair schemes for specific (5,3)- and (6,4)-Reed-Solomon (RS) codes. Further, we present a repair strategy for the RS code currently deployed in the Facebook Analytics Hadoop cluster that leads to 20 of repair BW savings over naive repair which is the repair scheme currently used for this code.