TFR-LRC: Rack-Optimized Locally Repairable Codes: Balancing Fault Tolerance, Repair Degree, and Topology Awareness in Distributed Storage Systems

Locally Repairable Codes (LRCs) have become the dominant design in wide-stripe erasure coding storage systems due to their excellent locality and low repair bandwidth. In such systems, the repair degree—defined as the number of helper nodes contacted during data recovery—is a key performance metric. However, as stripe width increases, the probability of multiple simultaneous node failures grows, which significantly raises the repair degree in traditional LRCs. Addressing this challenge, we propose a new family of codes called TFR-LRCs (Locally Repairable Codes for balancing fault tolerance and repair efficiency). TFR-LRCs introduce flexible design choices that allow trade-offs between fault tolerance and repair degree: they can reduce the repair degree by slightly increasing storage overhead, or enhance fault tolerance by tolerating a slightly higher repair degree. We design a matrix-based construction to generate TFR-LRCs and evaluate their performance through extensive simulations. The results show that, under multiple failure scenarios, TFR-LRC reduces the repair degree by up to 35% compared with conventional LRCs, while preserving the original LRC structure. Moreover, under identical code parameters, TFR-LRC achieves improved fault tolerance, tolerating up to g+2 failures versus g+1 in conventional LRCs, with minimal additional cost. Notably, in maintenance mode, where entire racks may become temporarily unavailable, TFR-LRC demonstrates substantially better recovery efficiency compared to existing LRC schemes, making it a practical choice for real-world deployments.

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Published
2025-09-15
DOI
https://doi.org/10.3390/info16090803
Primary Topic
Advanced Data Storage Technologies
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article

TFR-LRC: Rack-Optimized Locally Repairable Codes: Balancing Fault Tolerance, Repair Degree, and Topology Awareness in Distributed Storage Systems

Yan Wang, Yanhua Cao, Junhao Shi
Information
Advanced Data Storage Technologies
article

TFR-LRC: Rack-Optimized Locally Repairable Codes: Balancing Fault Tolerance, Repair Degree, and Topology Awareness in Distributed Storage Systems

Yan Wang, Yanhua Cao, Junhao Shi
article en

Abstract

Locally Repairable Codes (LRCs) have become the dominant design in wide-stripe erasure coding storage systems due to their excellent locality and low repair bandwidth. In such systems, the repair degree—defined as the number of helper nodes contacted during data recovery—is a key performance metric. However, as stripe width increases, the probability of multiple simultaneous node failures grows, which significantly raises the repair degree in traditional LRCs. Addressing this challenge, we propose a new family of codes called TFR-LRCs (Locally Repairable Codes for balancing fault tolerance and repair efficiency). TFR-LRCs introduce flexible design choices that allow trade-offs between fault tolerance and repair degree: they can reduce the repair degree by slightly increasing storage overhead, or enhance fault tolerance by tolerating a slightly higher repair degree. We design a matrix-based construction to generate TFR-LRCs and evaluate their performance through extensive simulations. The results show that, under multiple failure scenarios, TFR-LRC reduces the repair degree by up to 35% compared with conventional LRCs, while preserving the original LRC structure. Moreover, under identical code parameters, TFR-LRC achieves improved fault tolerance, tolerating up to g+2 failures versus g+1 in conventional LRCs, with minimal additional cost. Notably, in maintenance mode, where entire racks may become temporarily unavailable, TFR-LRC demonstrates substantially better recovery efficiency compared to existing LRC schemes, making it a practical choice for real-world deployments.

InformationVol. 16(9)
East China Jiaotong University (CN)
Openalex Percentile: Top 71%
Advanced Data Storage Technologies
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TFR-LRC: Rack-Optimized Locally Repairable Codes: Balancing Fault Tolerance, Repair Degree, and Topology Awareness in Distributed Storage Systems — Yan Wang, Yanhua Cao, et al. · Information (2025) | TGRS Research Map | TGRS