Data Replication Optimization Using Simulated Annealing

  • Chee Keong WeeEmail author
  • Richi NayakEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1127)


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.


  1. 1.
    King, E.: Automated Database Refresh in Very Large and Highly Replicated Environments (2011)Google Scholar
  2. 2.
    Simitsis, A., Vassiliadis, P., Sellis, T.: Optimizing ETL processes in data warehouses. IEEE (2005)Google Scholar
  3. 3.
    Software, Q.: Shareplex 9.0 Reference Guide (2018)Google Scholar
  4. 4.
    Gupta, R.: Introduction to Oracle GoldenGate (OGG). In: Gupta, R. (ed.) Mastering Oracle GoldenGate, pp. 3–10. Springer, Heidelberg (2016). Scholar
  5. 5.
    Gill, N.K., Singh, S.: A dynamic, cost-aware, optimized data replication strategy for heterogeneous cloud data centers. Future Gener. Comput. Syst. 65, 10–32 (2016)CrossRefGoogle Scholar
  6. 6.
    Chopard, B., Tomassini, M.: Simulated annealing. In: Chopard, B., Tomassini, M. (eds.) An Introduction to Metaheuristics for Optimization, pp. 59–79. Springer, Cham (2018). Scholar
  7. 7.
    Software, Q.: SharePlex 9.0 - Reference Guide. Quest Support (2018)Google Scholar
  8. 8.
    Souravlas, S., Sifaleras, A.: Trends in data replication strategies: a survey. Int. J. Parallel Emergent Distrib. Syst. 34, 1–18 (2017)Google Scholar
  9. 9.
    Hamdeni, C., Hamrouni, T., Charrada, F.B.: Evaluation of site availability exploitation towards performance optimization in data grids. Clust. Comput. 21(4), 1967–1980 (2018)CrossRefGoogle Scholar
  10. 10.
    Nazir, B., et al.: The impact of the implementation cost of replication in data grid job scheduling. Math. Comput. Appl. 23(2), 28 (2018)MathSciNetGoogle Scholar
  11. 11.
    Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Comput. Surv. (CSUR) 35(3), 268–308 (2003)CrossRefGoogle Scholar
  12. 12.
    Assadi, M.T., Bagheri, M.: Differential evolution and Population-based simulated annealing for truck scheduling problem in multiple door cross-docking systems. Comput. Ind. Eng. 96, 149–161 (2016)CrossRefGoogle Scholar
  13. 13.
    Samora, I., et al.: Simulated annealing in optimization of energy production in a water supply network. Water Resour. Manag. 30(4), 1533–1547 (2016)CrossRefGoogle Scholar
  14. 14.
    Zaretalab, A., et al.: A knowledge-based archive multi-objective simulated annealing algorithm to optimize series–parallel system with choice of redundancy strategies. Comput. Ind. Eng. 80, 33–44 (2015)CrossRefGoogle Scholar
  15. 15.
    Connell, A.M.: An analysis of database replication technologies with regard to Deep Space Network application requirements (2011)Google Scholar
  16. 16.
    Bahl, A.: Use Case to S/4HANA Smart Data Integration (SDI), SAP Editor (2018)Google Scholar
  17. 17.
    Quest Software: Shareplex for Oracle v9.1.4 (2018)Google Scholar
  18. 18.
    Brunt, B.: Going for gold: Dell Software’s SharePlex database replication offering is a powerful tool with a small footprint. Computer Reseller News (UK), p. 23 (2016)Google Scholar
  19. 19.
    Dell Software Extends SharePlex to Optimize Data Integration and Analysis. Information Technology Newsweekly, p. 136 (2013)Google Scholar
  20. 20.
    Quest Software Releases SharePlex v9, in ICT Monitor Worldwide U6 - ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8& U7 - Newspaper Article, SyndiGate Media Inc., Amman (2017)Google Scholar
  21. 21.
    Nikolaev, A.G., Jacobson, S.H.: Simulated annealing. In: Gendreau, M., Potvin, J.Y. (eds.) Handbook of Metaheuristics, vol. 146, pp. 1–39. Springer, Boston (2010)CrossRefGoogle Scholar
  22. 22.
    Quest Software: SharePlex 9.0 - Administration Guide (2019)Google Scholar
  23. 23.
    Milani, B.A., Navimipour, N.J.: A comprehensive review of the data replication techniques in the cloud environments: Major trends and future directions. J. Netw. Comput. Appl. 64, 229–238 (2016)CrossRefGoogle Scholar
  24. 24.
    Al-Betar, M.A.: β-Hill climbing: an exploratory local search. Neural Comput. Appl. 28(1), 153–168 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Electrical Engineering and Computer Science, Science and Engineering FacultyQueensland University of TechnologyBrisbaneAustralia

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