Task Scheduling and Resource Management Strategy for Edge Cloud Computing Using Improved Genetic Algorithm


Xiuye Yin, Liyong Chen, Journal of Information Processing Systems Vol. 19, No. 4, pp. 450-464, Aug. 2023  

https://doi.org/10.3745/JIPS.01.0095
Keywords: Edge Cloud Computing, energy consumption, Improved genetic algorithm, Normal Distribution Crossover Operator, resource management, task scheduling, time delay
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Abstract

To address the problems of large system overhead and low timeliness when dealing with task scheduling in mobile edge cloud computing, a task scheduling and resource management strategy for edge cloud computing based on an improved genetic algorithm was proposed. First, a user task scheduling system model based on edge cloud computing was constructed using the Shannon theorem, including calculation, communication, and network models. In addition, a multi-objective optimization model, including delay and energy consumption, was constructed to minimize the sum of two weights. Finally, the selection, crossover, and mutation operations of the genetic algorithm were improved using the best reservation selection algorithm and normal distribution crossover operator. Furthermore, an improved legacy algorithm was selected to deal with the multi-objective problem and acquire the optimal solution, that is, the best computing task scheduling scheme. The experimental analysis of the proposed strategy based on the MATLAB simulation platform shows that its energy loss does not exceed 50 J, and the time delay is 23.2 ms, which are better than those of other comparison strategies.


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Cite this article
[APA Style]
Yin, X. & Chen, L. (2023). Task Scheduling and Resource Management Strategy for Edge Cloud Computing Using Improved Genetic Algorithm. Journal of Information Processing Systems, 19(4), 450-464. DOI: 10.3745/JIPS.01.0095.

[IEEE Style]
X. Yin and L. Chen, "Task Scheduling and Resource Management Strategy for Edge Cloud Computing Using Improved Genetic Algorithm," Journal of Information Processing Systems, vol. 19, no. 4, pp. 450-464, 2023. DOI: 10.3745/JIPS.01.0095.

[ACM Style]
Xiuye Yin and Liyong Chen. 2023. Task Scheduling and Resource Management Strategy for Edge Cloud Computing Using Improved Genetic Algorithm. Journal of Information Processing Systems, 19, 4, (2023), 450-464. DOI: 10.3745/JIPS.01.0095.