LI Kunlun and WANG Jun, “Multi-objective Optimization for Cloud Task Scheduling Based on the ANP Model,” Chinese Journal of Electronics, vol. 26, no. 5, pp. 889-898, 2017, doi: 10.1049/cje.2017.07.019
Citation: LI Kunlun and WANG Jun, “Multi-objective Optimization for Cloud Task Scheduling Based on the ANP Model,” Chinese Journal of Electronics, vol. 26, no. 5, pp. 889-898, 2017, doi: 10.1049/cje.2017.07.019

Multi-objective Optimization for Cloud Task Scheduling Based on the ANP Model

doi: 10.1049/cje.2017.07.019
Funds:  This work is supported by the National Science and Technology Support Program (No.2013BAK07B00), the Natural Science Foundation of Heibei Province of China (No.F2013201170), and the Educational Commission of Hebei Province of China (No.ZD2014008).
  • Received Date: 2015-07-23
  • Rev Recd Date: 2015-12-06
  • Publish Date: 2017-09-10
  • We propose a multi-objective optimization algorithm for cloud task scheduling based on the Analytic network process (ANP) model to solve the problems in cloud task scheduling, such as the deficiencies of mathematical description, limited optimization abilities of the traditional multi-objective optimization algorithm and the selection of the Pareto optimal solutions. Firstly, we present the mathematical description of cloud task scheduling using matrix theory. Then, the improved Nondominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) multiobjective evolutionary algorithm whose optimization ability is improved by Gene expression programming (GEP) algorithm has been introduced into the cloud task scheduling field to search the Pareto set among multi-objects. Finally, ANP model has been combined with the improved NSGA-Ⅱ to solve the selection problems of Pareto solutions. Comparing with the multi-objective optimization algorithm based on the weighted polynomial, the proposed algorithm can optimize multiple goals at the same time, and can avoid the additional iterations due to the change of users preferences effectively. The simulation results indicate that the proposed algorithm is effective.
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