Scalable constructions of fractional repetition codes in distributed storage systems

TL;DR

This paper presents scalable methods for constructing fractional repetition codes in distributed storage, enabling efficient node repair and system expansion using combinatorial designs based on bipartite cage graphs and Latin squares.

Contribution

It introduces explicit algorithms for designing storage systems with large node sizes and low repair complexity, leveraging projective geometries and combinatorial structures.

Findings

Designs minimize storage nodes for given parameters

Systems allow easy expansion without reconfiguration

Construction guarantees efficient and exact node repair

Abstract

In distributed storage systems built using commodity hardware, it is necessary to have data redundancy in order to ensure system reliability. In such systems, it is also often desirable to be able to quickly repair storage nodes that fail. We consider a scheme--introduced by El Rouayheb and Ramchandran--which uses combinatorial block design in order to design storage systems that enable efficient (and exact) node repair. In this work, we investigate systems where node sizes may be much larger than replication degrees, and explicitly provide algorithms for constructing these storage designs. Our designs, which are related to projective geometries, are based on the construction of bipartite cage graphs (with girth 6) and the concept of mutually-orthogonal Latin squares. Via these constructions, we can guarantee that the resulting designs require the fewest number of storage nodes for the given parameters, and can further show that these systems can be easily expanded without need for frequent reconfiguration.