Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
169 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Fast Product-Matrix Regenerating Codes (1412.3022v1)

Published 9 Dec 2014 in cs.DC, cs.IT, cs.PF, and math.IT

Abstract: Distributed storage systems support failures of individual devices by the use of replication or erasure correcting codes. While erasure correcting codes offer a better storage efficiency than replication for similar fault tolerance, they incur higher CPU consumption, higher network consumption and higher disk I/Os. To address these issues, codes specific to storage systems have been designed. Their main feature is the ability to repair a single lost disk efficiently. In this paper, we focus on one such class of codes that minimize network consumption during repair, namely regenerating codes. We implement the original Product-Matrix Regenerating codes as well as a new optimization we propose and show that the resulting optimized codes allow achieving 790 MB/s for encoding in typical settings. Reported speeds are significantly higher than previous studies, highlighting that regenerating codes can be used with little CPU penalty.

Citations (1)

Summary

We haven't generated a summary for this paper yet.