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Data Replication Optimization Using Simulated Annealing

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Data Mining (AusDM 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1127))

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Abstract

Data replication is ubiquitous in a large organization where multiple IT systems need to share information for their operation. This function is usually fulfilled by an enterprise replicating software that is dependent on the configuration that the IT administrator sets. The setup specifies the tables and routes, but it may not be optimum to meet the workload, leading to replication’s lag and bottlenecks. This paper proposes an approach to solving the configuration optimization problem for the data replication software with the simulated-annealing based heuristic. Empirical results show that the configuration setting enables the replicating software to perform at least 5 times better than the baseline configuration set achieved by this approach.

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Author information

Authors and Affiliations

  1. School of Electrical Engineering and Computer Science, Science and Engineering Faculty, Queensland University of Technology, Brisbane, QLD, Australia

    Chee Keong Wee & Richi Nayak

Authors
  1. Chee Keong Wee
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  2. Richi Nayak
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Corresponding authors

Correspondence to Chee Keong Wee or Richi Nayak .

Editor information

Editors and Affiliations

  1. School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, Australia

    Thuc D. Le

  2. La Trobe University, Melbourne, Australia

    Kok-Leong Ong

  3. CSIRO Scientific Computing, Canberra, Australia

    Yanchang Zhao

  4. CSIRO Scientific Computing, Canberra, Australia

    Warren H. Jin

  5. Consilium Technology, Adelaide, Australia

    Sebastien Wong

  6. School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, Australia

    Lin Liu

  7. Microsoft Proprietary Limited, Singapore, Singapore

    Graham Williams

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Wee, C.K., Nayak, R. (2019). Data Replication Optimization Using Simulated Annealing. In: Le, T., et al. Data Mining. AusDM 2019. Communications in Computer and Information Science, vol 1127. Springer, Singapore. https://doi.org/10.1007/978-981-15-1699-3_18

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  • DOI: https://doi.org/10.1007/978-981-15-1699-3_18

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-1698-6

  • Online ISBN: 978-981-15-1699-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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