Journal
KNOWLEDGE-BASED SYSTEMS
Volume 169, Issue -, Pages 39-52Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.knosys.2019.01.023
Keywords
Task scheduling; Cloud computing; Makespan; Moth search algorithm (MSA); Differential evolution (DE); Meta-heuristics algorithm
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Funding
- National Key Research and Development Program of China [2017YFB1402203]
- Science & Technology Cooperation Program of Henan Province [152106000048]
- Hubei Provincial Natural Science Foundation of China [2017CFA012]
- Key Technical Innovation Project of Hubei Provence of China [2017AAA122]
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This paper presents an alternative method for cloud task scheduling problem which aims to minimize makespan that required to schedule a number of tasks on different Virtual Machines (VMs). The proposed method is based on the improvement of the Moth Search Algorithm (MSA) using the Differential Evolution (DE). The MSA simulates the behavior of moths to fly towards the source of light in nature through using two concepts, the phototaxis and Levy flights that represent the exploration and exploitation ability respectively. However, the exploitation ability is still needed to be improved, therefore, the DE can be used as local search method. In order to evaluate the performance of the proposed MSDE algorithm, a set of three experimental series are performed. The first experiment aims to compare the traditional MSA and the proposed algorithm to solve a set of twenty global optimization problems. Meanwhile, in second and third experimental series the performance of the proposed algorithm to solve the cloud task scheduling problem is compared against other heuristic and meta-heuristic algorithms for synthetical and real trace data, respectively. The results of the two experimental series show that the proposed algorithm outperformed other algorithms according to the performance measures. (C) 2019 Elsevier B.V. All rights reserved.
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