Discreet method to match safe site-pairs in short computation time for risk-aware data replication

Takaki Nakamura, Shinya Matsumoto, Hiroaki Muraoka

Research output: Contribution to journalReview articlepeer-review

7 Citations (Scopus)

Abstract

Risk-aware Data Replication (RDR), which replicates data at primary sites to nearby safe backup sites, has been proposed to mitigate service disruption in a disaster area even after a widespread disaster that damages a network and a primary site. RDR assigns a safe backup site to a primary site while considering damage risk for both the primary site and the backup candidate site. To minimize the damage risk of all site-pairs the Integer Programing Problem (IPP), which is a mathematical optimization problem, is applied. A challenge for RDR is to choose safe backup sites within a short computation time even for a huge number of sites. As described in this paper, we propose a Discreet method for RDR to surmount this hurdle. The Discreet method first judges the backup sites of a potentially unsafe primary site and avoids assigning a very safe primary site with a very safe backup site. We evaluated the computation time for site-paring and the data availability in the cases of Earthquake and Tsunami using basic disaster simulations. We confirmed that the computation rate of the proposed method is more than 1000 times faster than the existing method when the number of sites is greater than 1000. We also confirmed the data availability of the proposed method; it provides almost equal rates to existing methods of strict optimization. These results mean that the proposed method makes RDR more practical for massively multiple sites.

Original languageEnglish
Pages (from-to)1493-1502
Number of pages10
JournalIEICE Transactions on Information and Systems
VolumeE98D
Issue number8
DOIs
Publication statusPublished - 2015 Aug 1

Keywords

  • Availability
  • Disaster recovery
  • Distributed storage
  • Integer programming problem
  • Remote replication

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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