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Secure Scientific Applications Scheduling Technique for Cloud Computing Environment Using Global League Championship Algorithm

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  • Shafi’i Muhammad Abdulhamid
  • Muhammad Shafie Abd Latiff
  • Gaddafi Abdul-Salaam
  • Syed Hamid Hussain Madni

Abstract

Cloud computing system is a huge cluster of interconnected servers residing in a datacenter and dynamically provisioned to clients on-demand via a front-end interface. Scientific applications scheduling in the cloud computing environment is identified as NP-hard problem due to the dynamic nature of heterogeneous resources. Recently, a number of metaheuristics optimization schemes have been applied to address the challenges of applications scheduling in the cloud system, without much emphasis on the issue of secure global scheduling. In this paper, scientific applications scheduling techniques using the Global League Championship Algorithm (GBLCA) optimization technique is first presented for global task scheduling in the cloud environment. The experiment is carried out using CloudSim simulator. The experimental results show that, the proposed GBLCA technique produced remarkable performance improvement rate on the makespan that ranges between 14.44% to 46.41%. It also shows significant reduction in the time taken to securely schedule applications as parametrically measured in terms of the response time. In view of the experimental results, the proposed technique provides better-quality scheduling solution that is suitable for scientific applications task execution in the Cloud Computing environment than the MinMin, MaxMin, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) scheduling techniques.

Suggested Citation

  • Shafi’i Muhammad Abdulhamid & Muhammad Shafie Abd Latiff & Gaddafi Abdul-Salaam & Syed Hamid Hussain Madni, 2016. "Secure Scientific Applications Scheduling Technique for Cloud Computing Environment Using Global League Championship Algorithm," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-18, July.
    • Handle: RePEc:plo:pone00:0158102
    DOI: 10.1371/journal.pone.0158102
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    References listed on IDEAS

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    1. Yuchen Pan & Shuai Ding & Wenjuan Fan & Jing Li & Shanlin Yang, 2015. "Trust-Enhanced Cloud Service Selection Model Based on QoS Analysis," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-19, November.
    2. Hugh P Shanahan & Anne M Owen & Andrew P Harrison, 2014. "Bioinformatics on the Cloud Computing Platform Azure," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-9, July.
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    Cited by:

    1. Jianguo Zheng & Yilin Wang, 2021. "A Hybrid Multi-Objective Bat Algorithm for Solving Cloud Computing Resource Scheduling Problems," Sustainability, MDPI, vol. 13(14), pages 1-25, July.
    2. Syed Hamid Hussain Madni & Muhammad Shafie Abd Latiff & Mohammed Abdullahi & Shafi’i Muhammad Abdulhamid & Mohammed Joda Usman, 2017. "Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-26, May.

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