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An improved genetic algorithm using greedy strategy toward task scheduling optimization in cloud environments

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Abstract

Cloud computing is an emerging distributed system that provides flexible and dynamically scalable computing resources for use at low cost. Task scheduling in cloud computing environment is one of the main problems that need to be addressed in order to improve system performance and increase cloud consumer satisfaction. Although there are many task scheduling algorithms, existing approaches mainly focus on minimizing the total completion time while ignoring workload balancing. Moreover, managing the quality of service (QoS) of the existing approaches still needs to be improved. In this paper, we propose a novel algorithm named MGGS (modified genetic algorithm (GA) combined with greedy strategy). The proposed algorithm leverages the modified GA algorithm combined with greedy strategy to optimize task scheduling process. Different from existing algorithms, MGGS can find an optimal solution using fewer number of iterations. To evaluate the performance of MGGS, we compared the performance of the proposed algorithm with several existing algorithms based on the total completion time, average response time, and QoS parameters. The results obtained from the experiments show that MGGS performs well as compared to other task scheduling algorithms.

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Acknowledgement

This work was supported by the National Natural Science Foundation of China (Nos. 61572525 and 61772088), the China Postdoctoral Science Foundation (No. 2018M642974), the Scientific research project of education department of hunan province, the Natural Science Foundation of Hunan Province (No. 2019JJ50689) and the Science and Technology Plan Project of Changsha city (No. k1705036).

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, Changsha University, Changsha, 410083, China

    Zhou Zhou & Fangmin Li

  2. Department of Computer Science, Hunan University, Changsha, 410083, China

    Zhou Zhou

  3. Information Engineering Department, Zhangjiajie Institute of Aeronautical Engineering, Zhangjiajie, 427000, China

    Huaxi Zhu & Houliang Xie

  4. School of Information Technology, Deakin University, Geelong, VIC, 3217, Australia

    Jemal H. Abawajy & Morshed U. Chowdhury

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  1. Zhou Zhou
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  2. Fangmin Li
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Correspondence to Houliang Xie.

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Zhou Zhou and Huaxi Zhu are co-first authors.

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Zhou, Z., Li, F., Zhu, H. et al. An improved genetic algorithm using greedy strategy toward task scheduling optimization in cloud environments. Neural Comput & Applic 32, 1531–1541 (2020). https://doi.org/10.1007/s00521-019-04119-7

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