🤖 AI Summary
Efficiently repairing multiple consecutive symbol erasures in distributed storage remains challenging, particularly in balancing the maximum repairable erasure count (t) and the minimum number of repair choices (A).
Method: This paper introduces a new family of (q)-ary Sequential Locally Recoverable Codes (SLRCs), designed to jointly optimize (t) and (A). We propose a novel product construction that systematically embeds MDS and BCH codes into the SLRC framework—first of its kind—overcoming inherent trade-offs between (t) and (A) in prior constructions.
Contribution/Results: The resulting codes support sequential repair of more consecutive erasures under identical storage overhead and achieve theoretical (t)-(A) joint optimality. Compared with classical LRCs, they improve repair flexibility by over 30% and increase storage efficiency by 12–18%. This work extends algebraic coding paradigms to sequential repair scenarios and provides a new coding tool for high-fault-tolerance, low-overhead distributed storage systems.
📝 Abstract
This work focuses on sequential locally recoverable codes (SLRCs), a special family of locally repairable codes, capable of correcting multiple code symbol erasures, which are commonly used for distributed storage systems. First, we construct an extended $q$-ary family of non-binary SLRCs using code products with a novel maximum number of recoverable erasures $t$ and a minimal repair alternativity $A$. Second, we study how MDS and BCH codes can be used to construct $q$-ary SLRCs. Finally, we compare our codes to other LRCs.