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Variable neighborhood search based multiobjective ACO-list scheduling for cloud workflows

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Abstract

Effective workflow scheduling is essential to obtain high execution performance of workflow applications in cloud computing and remains a challenging problem. Due to the commercial nature of clouds, the execution cost of a workflow is a crucial issue for cloud users except for the execution time (makespan). We formulate the cloud workflow scheduling as a multiobjective optimization problem to minimize both execution cost and makespan. A Variable neighborhood search-based Multiobjective Ant colony optimization (ACO)-List Scheduling approach (VMALS) is proposed to address it. In VMALS, the list scheduling is first integrated into the ACO-based multiobjective optimization to consider the effect of different task scheduling sequences on the execution cost and makespan of a workflow. Then, a variable neighborhood search (VNS) is applied to nondominated solutions generated by ACO to approximate the true Pareto front better. Moreover, two novel crossover and mutation-based neighborhood structures are devised to enhance the local search capability of VNS. VMALS is compared with some state-of-the-art algorithms. Experimental results show that VMALS performs better than the comparative algorithms, and the average value of hypervolume metric of VMALS is 3.54–86.18% higher than that of comparative algorithms.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Notes

  1. https://confluence.pegasus.isi.edu/display/pegasus/WorkflowHub.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (61873040, 61973042, 62166027), in part by the Science and Technology Plan Project of Jiangxi Provincial Education Department (GJJ190959), and in part by Jiangxi Provincial Natural Science Foundation (20212ACB212004).

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Authors and Affiliations

  1. The School of Computing Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Yun Wang & Xingquan Zuo

  2. The School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China

    Yun Wang, Zhiqiang Wu & Hui Wang

  3. The School of Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Xinchao Zhao

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  1. Yun Wang
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Correspondence to Yun Wang.

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Wang, Y., Zuo, X., Wu, Z. et al. Variable neighborhood search based multiobjective ACO-list scheduling for cloud workflows. J Supercomput 78, 18856–18886 (2022). https://doi.org/10.1007/s11227-022-04616-y

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