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Collaborative Scheduling of Multi-cloud Distributed Multi-cloud Tasks Based on Evolutionary Multi-tasking Algorithm

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Bio-Inspired Computing: Theories and Applications (BIC-TA 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2061))

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Abstract

With the rapid development of information technology making the scale of the Internet increasing day by day, collaborative optimization of multiple scheduling tasks in a multi-cloud environment provides users with faster scheduling options. Meanwhile, there is a certain similarity between cloud scheduling tasks, and in order not to waste the similarity between tasks, similar tasks are linked together to find an optimal scheduling solution for multiple tasks, making it possible to handle multiple scheduling tasks simultaneously. Firstly, we construct a multi-objective optimization model considering time, cost and VM resource load balance; secondly, since there are not only independent optimization problems in real scenarios, we adapt the constructed multiple similar optimization models and propose a multi-task multi-objective optimization model; finally, to be able to solve the constructed model better, we use a proposed objective function-based Finally, we propose an evolutionary multitasking algorithm based on weighted summation of the objective functions, which allows the algorithm to find the optimal solution among multiple multi-objective models. Simulation experiments show that the proposed algorithm has better performance.

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Acknowledgements

This work is supported by the Science and Technology Development Foundation of the Central Guiding Local under Grant No. YDZJSX2021A038; National Natural Science Foundation of China under Grant No.61806138; Postgraduate Joint Training Demonstration Base of Taiyuan University of Science and Technology Fund (Grant NO. JD2022003).

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Authors and Affiliations

  1. Shanxi Laboratory of Big Data Analysis and Parallel Computing, Taiyuan University of Science and Technology, Taiyuan, 030024, China

    Tianhao Zhao, Linjie Wu, Zhihua Cui & Xingjuan Cai

Authors
  1. Tianhao Zhao
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  2. Linjie Wu
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  3. Zhihua Cui
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  4. Xingjuan Cai
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Corresponding author

Correspondence to Tianhao Zhao .

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Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  2. Central South University, Changsha, China

    Yong Wang

  3. Huazhong University of Science and Technology, Wuhan, China

    Jianqing Lin

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Zhao, T., Wu, L., Cui, Z., Cai, X. (2024). Collaborative Scheduling of Multi-cloud Distributed Multi-cloud Tasks Based on Evolutionary Multi-tasking Algorithm. In: Pan, L., Wang, Y., Lin, J. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2023. Communications in Computer and Information Science, vol 2061. Springer, Singapore. https://doi.org/10.1007/978-981-97-2272-3_1

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  • DOI: https://doi.org/10.1007/978-981-97-2272-3_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2271-6

  • Online ISBN: 978-981-97-2272-3

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