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A method of repairing single node failure in the distributed storage system based on the regenerating-code and a hybrid genetic algorithm

机译:一种基于再生码和混合遗传算法在分布式存储系统中修复单节点故障的方法

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摘要

To ensure the reliability and security of the data, the large-scale distributed storage system usually adopts the data redundancy mechanism to repair the data on the faulty nodes. Comparing with the replication-based method, the data redundancy mechanism of erasure code can effectively improve the use of storage space, but it may result in the large network overhead when recovering the data. The regenerating code is an improved erasure code, which can reduce the quantity of data transmission compared to that of the erasure code. Adopting the regenerating code to repair the data on a faulty node requires constructing an optimal repair tree to maximum the bandwidth of the bottleneck link, which is an NP-hard problem. To construct the optimal repair tree, a hybrid genetic algorithm is proposed in this paper. In particular, our proposal comprehensively considers the network topology and link bandwidth of storage nodes and designs a problem-specific cross-correlation operator, mutation operator and local search operator. In addition, we provided the mathematical proof of the global convergence with probability one with respect to the proposed hybrid genetic algorithm. Through a series of simulation experiments, the results show that our proposal is able to determine the optimal repair tree, which effectively reduces the delay of faulty node repair in distributed storage systems, and improve the repairing efficiency. CO 2020 Published by Elsevier B.V.
机译:为确保数据的可靠性和安全性,大规模分布式存储系统通常采用数据冗余机制来修复故障节点上的数据。与基于复制的方法相比,擦除代码的数据冗余机制可以有效地改善存储空间的使用,但是在恢复数据时可能导致大网络开销。再生代码是一种改进的擦除代码,其可以减少与擦除代码相比的数据传输量。采用重新生成代码修复故障节点上的数据需要构建最佳修复树,以最大限度地为瓶颈链路的带宽,这是一个NP难题。为了构建最佳修复树,本文提出了一种混合遗传算法。特别是,我们的提议全面地考虑了存储节点的网络拓扑和链接带宽,并设计了特定于特定于问题的互相关运算符,突变运算符和本地搜索操作员。此外,我们提供了相对于提出的混合遗传算法的概率与概率的数学证明。通过一系列仿真实验,结果表明我们的建议能够确定最佳修复树,这有效地降低了分布式存储系统中故障节点修复的延迟,提高了修复效率。 CO 2020由Elsevier B.V发布。

著录项

  • 来源
    《Neurocomputing》 |2021年第11期|566-578|共13页
  • 作者单位

    Guilin Univ Elect Technol Sch Informat & Commun 1 Jinji Rd Guilin 541004 Peoples R China|Guilin Univ Elect Technol Minist Educ Key Lab Cognit Radio & Informat Proc 1 Jinji Rd Guilin 541004 Peoples R China;

    Guilin Univ Elect Technol Sch Informat & Commun 1 Jinji Rd Guilin 541004 Peoples R China;

    Guilin Univ Elect Technol Sch Informat & Commun 1 Jinji Rd Guilin 541004 Peoples R China;

    Guilin Univ Elect Technol Sch Informat & Commun 1 Jinji Rd Guilin 541004 Peoples R China;

    Guilin Univ Elect Technol Sch Informat & Commun 1 Jinji Rd Guilin 541004 Peoples R China|54th Res Inst China Elect Technol Grp Corp 589 West Zhongshan Rd Shijiazhuang 050081 Hebei Peoples R China;

    Beijing Normal Univ Dept Phys 19 XinJieKouWai St Beijing 100875 Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Distributed storage system; Erasure code; Regenerating code; Hybrid Genetic algorithm; Optimal repair tree;

    机译:分布式存储系统;擦除代码;再生代码;混合遗传算法;最优修复树;

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