Proceedings of the 2nd International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2023, June 2–4, 2023, Nanchang, China

Research Article

A Cloud Computing Task Scheduling Method Based on Genetic Algorithm

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  • @INPROCEEDINGS{10.4108/eai.2-6-2023.2334608,
        author={Huabin  Zhang},
        title={A Cloud Computing Task Scheduling Method Based on Genetic Algorithm},
        proceedings={Proceedings of the 2nd International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2023, June 2--4, 2023, Nanchang, China},
        publisher={EAI},
        proceedings_a={ICIDC},
        year={2023},
        month={8},
        keywords={cloud computing genetic algorithm task scheduling},
        doi={10.4108/eai.2-6-2023.2334608}
    }
    
  • Huabin Zhang
    Year: 2023
    A Cloud Computing Task Scheduling Method Based on Genetic Algorithm
    ICIDC
    EAI
    DOI: 10.4108/eai.2-6-2023.2334608
Huabin Zhang1,*
  • 1: Shandong University of Science and Technology
*Contact email: 1579401432@qq.com

Abstract

In order to solve the traditional Internet service ability and expanding an acute shortage of, cloud computing has come into being, and has universal service in today's Internet services. Due to the huge scale of cloud computing and the diversity of applications and tasks, the performance requirements of cloud computing have been significantly improved. The characteristics of cloud computing brings the challenge of task scheduling, affect the performance and reliability of the system. To solve these problems, this paper puts forward cloud computing task scheduling algorithm based on genetic algorithm method. Firstly, the model of task and cloud computing resources and the objective function were established. Then, based on the task and cloud resource model, the gene genetic algorithm for task scheduling was proposed. Finally, through the experiment show that the algorithm has good results on the cloud resource scheduling, can reduce the total cost of users, to reduce the energy consumption.