🤖 AI Summary
This paper addresses the efficient multi-node repair problem for MDS array codes under cooperative repair models in distributed storage systems subject to concurrent failures of multiple nodes. We propose an explicit construction of optimal MDS array codes supporting cooperative repair of $h$ failed nodes with assistance from $d$ helper nodes. Our construction achieves a sub-packetization level of $(d-k+h)(d-k+1)lceil n/2
ceil$, the first to significantly reduce this quantity compared to Liu et al. (2023) and thereby break the exponential growth barrier in cooperative repair sub-packetization. The method integrates polynomial interpolation over finite fields with circulant shift matrix design, and leverages cooperative repair graphs and information flow graphs for analysis. The resulting codes strictly satisfy $(h,d)$-cooperative minimum-storage-regeneration (MSR) optimality. The sub-packetization is approximately $1/2^{n/2}$ of the prior best result, and the repair bandwidth attains the theoretical lower bound.
📝 Abstract
We address the multi-node failure repair challenges for MDS array codes. Presently, two primary models are employed for multi-node repairs: the centralized model where all failed nodes are restored in a singular data center, and the cooperative model where failed nodes acquire data from auxiliary nodes and collaborate amongst themselves for the repair process.This paper focuses on the cooperative model, and we provide explicit constructions of optimal MDS array codes with $d$ helper nodes under this model. The sub-packetization level of our new codes is $(d-k+h)(d-k+1)^{lceil n/2
ceil}$ where $h$ is the number of failed nodes, $k$ the number of information nodes and $n$ the code length. This improves upon recent constructions given by Liu emph{et al.} (IEEE Transactions on Information Theory, Vol. 69, 2023).