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Clustered Distributed Data Storage Repairing Multiple Failures.

Shiqiu Liu1, Fangwei Ye2, Qihui Wu3

  • 1Pengcheng Laboratory, Shenzhen 518066, China.

Entropy (Basel, Switzerland)
|March 28, 2025
PubMed
Summary
This summary is machine-generated.

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This study optimizes clustered distributed storage systems (DSS) for efficient data repair. It introduces collaborative repair within clusters, reducing bandwidth costs for multiple node failures.

Area of Science:

  • Computer Science
  • Data Storage Systems

Background:

  • Clustered distributed storage systems (DSS) group nodes into clusters, impacting data repair costs based on node location.
  • Intra-cluster communication is significantly cheaper than inter-cluster communication, crucial for efficient node repair.

Purpose of the Study:

  • To analyze data repair strategies in clustered DSS, focusing on intra-cluster data retrieval.
  • To investigate collaborative repair for multiple failures within the same cluster.
  • To derive the storage-repair bandwidth trade-off for clustered DSS.

Main Methods:

  • Modeling clustered distributed storage systems with restricted inter-cluster connectivity.
  • Analyzing scenarios where failed nodes retrieve data exclusively from their own cluster.
  • Developing explicit code constructions for optimal repair points.
Keywords:
clusteredstorage systemminimum bandwidthminimum storageregenerating code

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Main Results:

  • Derived the storage versus repair bandwidth trade-off for clustered DSS with intra-cluster repair.
  • Provided code constructions for the minimum storage clustered collaborative repair (MSCCR) point.
  • Provided code constructions for the minimum bandwidth clustered collaborative repair (MBCCR) point.

Conclusions:

  • Intra-cluster repair significantly enhances efficiency in clustered DSS, especially for multiple failures.
  • The derived trade-off provides a framework for optimizing storage and repair bandwidth.
  • Explicit code constructions demonstrate achievable optimal repair strategies.