Maximally Recoverable Codes for Grid-like Topologies
Parikshit Gopalan, Guangda Hu, Swastik Kopparty, Shubhangi Saraf,, Carol Wang, Sergey Yekhanin

TL;DR
This paper studies the design of maximally recoverable codes for grid-like topologies in distributed storage, addressing the tradeoff between reliability and efficiency, and establishing new lower bounds on field size for such codes.
Contribution
It introduces grid-like topologies for storage codes, analyzes the reliability/efficiency tradeoff, and proves the first super-polynomial lower bound on field size for maximally recoverable codes.
Findings
Proposes grid-like topologies unifying existing models.
Analyzes the reliability/efficiency tradeoff in code design.
Establishes super-polynomial lower bounds on field size for MR codes.
Abstract
The explosion in the volumes of data being stored online has resulted in distributed storage systems transitioning to erasure coding based schemes. Yet, the codes being deployed in practice are fairly short. In this work, we address what we view as the main coding theoretic barrier to deploying longer codes in storage: at large lengths, failures are not independent and correlated failures are inevitable. This motivates designing codes that allow quick data recovery even after large correlated failures, and which have efficient encoding and decoding. We propose that code design for distributed storage be viewed as a two-step process. The first step is choose a topology of the code, which incorporates knowledge about the correlated failures that need to be handled, and ensures local recovery from such failures. In the second step one specifies a code with the chosen topology by choosing…
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Taxonomy
TopicsAdvanced Data Storage Technologies · Caching and Content Delivery · Distributed systems and fault tolerance
