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Multi-objective workflow scheduling in cloud system based on cooperative multi-swarm optimization algorithm

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Abstract

In order to improve the performance of multi-objective workflow scheduling in cloud system, a multi-swarm multiobjective optimization algorithm (MSMOOA) is proposed to satisfy multiple conflicting objectives. Inspired by division of the same species into multiple swarms for different objectives and information sharing among these swarms in nature, each physical machine in the data center is considered a swarm and employs improved multi-objective particle swarm optimization to find out non-dominated solutions with one objective in MSMOOA. The particles in each swarm are divided into two classes and adopt different strategies to evolve cooperatively. One class of particles can communicate with several swarms simultaneously to promote the information sharing among swarms and the other class of particles can only exchange information with the particles located in the same swarm. Furthermore, in order to avoid the influence by the elastic available resources, a manager server is adopted in the cloud data center to collect the available resources for scheduling. The quality of the proposed method with other related approaches is evaluated by using hybrid and parallel workflow applications. The experiment results highlight the better performance of the MSMOOA than that of compared algorithms.

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Authors and Affiliations

  1. Engineering Research Center of Digitized Textile & Fashion Technology of Ministry of Education, College of Information Sciences and Technology, Donghua University, Shanghai, 201620, China

    Guang-shun Yao  (姚光顺), Yong-sheng Ding  (丁永生) & Kuang-rong Hao  (郝矿荣)

  2. College of Computer and Information Engineering, Chuzhou University, Chuzhou, 239000, China

    Guang-shun Yao  (姚光顺)

Authors
  1. Guang-shun Yao  (姚光顺)
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  2. Yong-sheng Ding  (丁永生)
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  3. Kuang-rong Hao  (郝矿荣)
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Correspondence to Yong-sheng Ding  (丁永生).

Additional information

Foundation item: Project(61473078) supported by the National Natural Science Foundation of China; Project(2015-2019) supported by the Program for Changjiang Scholars from the Ministry of Education, China; Project(16510711100) supported by International Collaborative Project of the Shanghai Committee of Science and Technology, China; Project(KJ2017A418) supported by Anhui University Science Research, China

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Yao, Gs., Ding, Ys. & Hao, Kr. Multi-objective workflow scheduling in cloud system based on cooperative multi-swarm optimization algorithm. J. Cent. South Univ. 24, 1050–1062 (2017). https://doi.org/10.1007/s11771-017-3508-7

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