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Mobile Networks and Management. 12th EAI International Conference, MONAMI 2022, Virtual Event, October 29-31, 2022, Proceedings

Research Article

A Cloud-Side Task Scheduling Algorithm with Multiple Evaluation Metrics

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-32443-7_15,
        author={Yong Ma and Xinghong Jiang and Chenyang Lv and Tao Tao and Liang Zhou and Qilin Xie and Lingguo Zhen},
        title={A Cloud-Side Task Scheduling Algorithm with Multiple Evaluation Metrics},
        proceedings={Mobile Networks and Management. 12th EAI International Conference, MONAMI 2022, Virtual Event, October 29-31, 2022, Proceedings},
        proceedings_a={MONAMI},
        year={2023},
        month={5},
        keywords={Genetic algorithm Edge cloud Amulti-objective restriction task scheduling},
        doi={10.1007/978-3-031-32443-7_15}
    }
    
  • Yong Ma
    Xinghong Jiang
    Chenyang Lv
    Tao Tao
    Liang Zhou
    Qilin Xie
    Lingguo Zhen
    Year: 2023
    A Cloud-Side Task Scheduling Algorithm with Multiple Evaluation Metrics
    MONAMI
    Springer
    DOI: 10.1007/978-3-031-32443-7_15
Yong Ma1, Xinghong Jiang1, Chenyang Lv1, Tao Tao2, Liang Zhou,*, Qilin Xie3, Lingguo Zhen
  • 1: School of Computer and Information Engineering
  • 2: School of Digital Industry
  • 3: School of Electronic and Information Engineering
*Contact email: zhouliang@epri.sgcc.com.cn

Abstract

With the popularity of intelligent terminal devices, edge computing has been fully developed. Power patrol robot is widely used in power grid information collection, and edge computing can effectively shorten response time, improve processing efficiency and reduce network pressure, so as to meet the real-time requirements. However, the following problem is how to realize the scheduling strategy of edge cloud and central cloud and optimize multi performance indicators. To solve this problem, this paper proposes a task scheduling model combining genetic algorithm with Docker container technology and taking cloud computing center and edge cloud into comprehensive consideration. Firstly, the task is classified by condition analysis. Assign tasks to cloud computing centers or edge nodes according to the task type; Genetic algorithm is used to assign tasks to edge nodes. Finally, the performance of the model is verified in the simulation environment. The experimental results show that this task allocation method greatly improves the resource utilization of edge server equipment on the basis of considering the needs of tasks, the limited resources of edge server, and meeting the needs of task proposers.

Keywords
Genetic algorithm Edge cloud Amulti-objective restriction task scheduling
Published
2023-05-28
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-32443-7_15
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