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Endocrine-based coevolutionary multi-swarm for multi-objective workflow scheduling in a cloud system

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Abstract

The workflow scheduling with multiple objectives is a well-known NP-complete problem, and even more complex and challenging when the workflow is executed in cloud computing system. In this study, an endocrine-based coevolutionary multi-swarm for multi-objective optimization algorithm (ECMSMOO) is proposed to satisfy multiple scheduling conflicting objectives, such as the total execution time (makespan), cost, and energy consumption. To avoid the influence of elastic available resources, a manager server is adopted to collect the available resources for scheduling. In ECMSMOO, multi-swarms are adopted and each swarm employs improved multi-objective particle swarm optimization to find out non-dominated solutions with one objective. To avoid falling into local optima which is common in traditional heuristic algorithms, an endocrine-inspired mechanism is embedded in the particles’ evolution process. Furthermore, a competition and cooperation technique among swarms is designed in the ECMSMOO. All these strategies effectively improve the performance of ECMSMOO. We compare the quality of the proposed method with other algorithms for multi-objective task scheduling by hybrid and parallel workflow jobs. The results highlight the better performance of the proposed approach than that of the compared algorithms.

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Acknowledgments

This work was supported in part by the Key Project of the National Nature Science Foundation of China (No. 61134009), the National Nature Science Foundation of China (Nos. 61473077, 61473078), Cooperative research funds of the National Natural Science Funds Overseas and Hong Kong and Macao scholars (No. 61428302), Program for Changjiang Scholars from the Ministry of Education, Specialized Research Fund for Shanghai Leading Talents, Project of the Shanghai Committee of Science and Technology (No. 13JC1407500), Innovation Program of Shanghai Municipal Education Commission (No. 14ZZ067), the Fundamental Research Funds for the Central Universities (15D110423), Natural Science Foundation of Anhui Province (No. 1508085MF123), and Key Project of Anhui University Science Research (No. KJ2015A190).

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Authors and Affiliations

  1. Engineering Research Center of Digitized Textile and Apparel Technology, Ministry of Education, College of Information Science and Technology, Donghua University, Shanghai, 201620, People’s Republic of China

    Guangshun Yao, Yongsheng Ding, Yaochu Jin & Kuangrong Hao

  2. College of Computer and Information Engineering, Chuzhou University, Chuzhou, 239000, People’s Republic of China

    Guangshun Yao

  3. Department of Computing, University of Surrey, Guildford, GU2 7XH, UK

    Yaochu Jin

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  1. Guangshun Yao
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  2. Yongsheng Ding
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  3. Yaochu Jin
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  4. Kuangrong Hao
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Correspondence to Yongsheng Ding.

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The authors declare that they have no conflict of interest.

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Communicated by V. Loia.

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Yao, G., Ding, Y., Jin, Y. et al. Endocrine-based coevolutionary multi-swarm for multi-objective workflow scheduling in a cloud system. Soft Comput 21, 4309–4322 (2017). https://doi.org/10.1007/s00500-016-2063-8

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