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Multi-workflow Scheduling Based on Implicit Information Transmission in Cloud Computing Environment

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Bio-Inspired Computing: Theories and Applications (BIC-TA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1566))

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Abstract

In the current cloud computing environment, task scheduling and resource allocation are the key and difficult points in the performance improvement. However, there are numerous problems of workflow, such as Montage, Inspiral, Cybershake etc. They have similar workflow structures, which affect the efficiency of task scheduling and resource distribution. In addition, the result obtained by the traditional evolutionary algorithm is the allocation sequence of the virtual machine in the cloud computing environment only for single task, which is a great waste of resources. Aiming at these problems, the multiple workflow tasks are processed in this paper by using implicit information transfer at the same time, that is, to reasonably use the allocation sequence of each task to exchange information so as to share a better virtual machine allocation. Meanwhile, using the potential relationship and differences between different tasks are better able to make population has better convergence and diversity. We proposed a multifactorial evolutionary algorithm based on combinatorial population (CP-MFEA) for multitasking workflows. This paper constructs nine sets of multi-task combination problems, and compares the method with the traditional single-task evolutionary algorithm, the purpose is to describe the superiority of this method clearly. Through the experimental results, we can notice that CP-MFEA’s ability is much more obvious than single-task evolutionary algorithms.

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Acknowledgment

This work is supported by the National Key Research and Development Program of China under Grant No.2018YFC1604000, the National Natural Science Foundation of China under Grant No.61806138, No. U1636220, No.61961160707 and No.61976212, Key R&D program of Shanxi Province (International Cooperation) under Grant No.201903D421048. Australian Research Council (ARC) projects DP190101893, DP170100136, and LP180100758.

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Authors and Affiliations

  1. School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan, 030024, China

    Liangqian Ji, Yang Lan & Xingjuan Cai

  2. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Tingting Dong

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  1. Liangqian Ji
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  2. Tingting Dong
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  3. Yang Lan
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  4. Xingjuan Cai
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Correspondence to Xingjuan Cai .

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Editors and Affiliations

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

    Linqiang Pan

  2. Taiyuan University of Science and Technology, Taiyuan, China

    Zhihua Cui

  3. Taiyuan University of Science and Technology, Taiyuan, China

    Jianghui Cai

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

    Lianghao Li

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Ji, L., Dong, T., Lan, Y., Cai, X. (2022). Multi-workflow Scheduling Based on Implicit Information Transmission in Cloud Computing Environment. In: Pan, L., Cui, Z., Cai, J., Li, L. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2021. Communications in Computer and Information Science, vol 1566. Springer, Singapore. https://doi.org/10.1007/978-981-19-1253-5_7

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  • DOI: https://doi.org/10.1007/978-981-19-1253-5_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-1252-8

  • Online ISBN: 978-981-19-1253-5

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