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A Double-Objective Genetic Algorithm for Parity Declustering Optimization in Networked RAID

  • Xiaoguang Liu
  • Gang Wang
  • Jing Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4494)

Abstract

RAID, as a popular technology to improve the performance and reliability of storage system, has been used widely in computer industry. Recently, the technique of designing data layout in order to fit the requirements of networked storage is becoming a new challenge in this field. In this paper, we present a double-objective Genetic Algorithm for parity declustering optimization in networked RAID with a modified NSGA, we also take Distributed recovery workload and Distributed parity as two objects to find optimal data layout for parity declustering in networked RAID.

Keywords

Genetic Algorithm Multiobjective Optimization Simulated Annealing Algorithm Data Layout Multiobjective Genetic Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Xiaoguang Liu
    • 1
  • Gang Wang
    • 1
  • Jing Liu
    • 1
  1. 1.Department of Computer Science, Nankai University, Tianjin, 300071China

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