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A hybrid instance-intensive workflow scheduling method in private cloud environment

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Abstract

Aiming to solve the problem of instance-intensive workflow scheduling in private cloud environment, this paper first formulates a scheduling optimization model considering the communication time between tasks. The objective of this model is to minimize the execution time of all workflow instances. Then, a hybrid scheduling method based on the batch strategy and an improved genetic algorithm termed fragmentation based genetic algorithm is proposed according to the characters of instance-intensive cloud workflow, where task priority dispatching rules are also taken into account. Simulations are conducted to compare the proposed method with the canonical genetic algorithm and two heuristic algorithms. Our simulation results demonstrate that the proposed method can considerably enhance the search efficiency of the genetic algorithm and is able to considerably outperform the compared algorithms, in particular when the number of workflow instances is high and the computational resource available for optimization is limited.

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Acknowledgements

This research is partially supported by the National Natural Science Foundation of China (Grant No. 71001013), the Fundamental Research Funds for the Central Universities of China (Grant No. DUT13JS11), and the Joint Research Fund for Overseas Chinese, Hong Kong and Macao Scholars of the National Natural Science Foundation of China (Grant No. 61428302).

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Authors and Affiliations

  1. Institute of Information and Decision Technology, Dalian University of Technology, Dalian, Liaoning Province, People’s Republic of China

    Xin Ye, Jia Li, Sihao Liu, Jiwei Liang & Yaochu Jin

  2. Department of Computer Science, University of Surrey, Guildford, GU2 7XH, UK

    Yaochu Jin

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  1. Xin Ye
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  2. Jia Li
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  3. Sihao Liu
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  4. Jiwei Liang
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  5. Yaochu Jin
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Correspondence to Xin Ye.

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Ye, X., Li, J., Liu, S. et al. A hybrid instance-intensive workflow scheduling method in private cloud environment. Nat Comput 18, 735–746 (2019). https://doi.org/10.1007/s11047-016-9600-3

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  • DOI: https://doi.org/10.1007/s11047-016-9600-3

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