Cloud computing task resource allocation based on improved genetic algorithm

Xing Liu, Jiyang Han

Abstract


In view of the low delay and low energy consumption of cloud computing, in the intelligent manufacturing environment,
it can realize real-time analysis of massive manufacturing data and fast task processing. On this basis, taking cloud computing resource
allocation as the research object, an improved genetic algorithm is proposed. By increasing the variation, and doing targeted fi tness function
analysis of diff erent variations of the same gene fragment point, the excellent variation is compared to enter the next generation. A numerical
example shows that the proposed model and algorithm are both eff ective, and both iteration times and task completion time are obviously
superior to the classical genetic algorithm.

Keywords


cloud computing; Resource allocation; Improved genetic algorithms; Intelligent manufacturing


Included Database


References


[1] Liqiang Zhang,Jianrong Lv,Fei Yan etal. [J]. Journal of Zhengzhou University,2022,54(04): 1-11.

[2] Zhao Tong,Xiaomei Deng,Hongjian Chen etal. Multi-objective Task Scheduling algorithm based on reinforcement Learning in Cloud environment [J].

Minicomputer Systems, 2019,41(02):285-290.

[3] Zhandong Sun, Jiao Jiao, Wei Li etal. Research on Task Scheduling Strategy of Power Cloud Data Center based on improved Ant Colony Algorithm [J].

Power System Protection and Control,2022,50(02):95-101.

[4] Zhendao Wang,Yiming Zhang, Xueqian Shi. Cloud computing resource scheduling strategy based on competitive particle swarm optimization [J]. Journal

of Hunan University (Natural Science Edition),2021,48(06):80-87.

[5] Dengming Liu,Junfeng Jing,Kai Liu etal. Research on cloud computing resource allocation strategy based on improved ant colony algorithm [J].

Application of Electronic Technique,2022, 48(05): 104-109.

[6] Xuesen Ma,Jie Tan,Shuyou Chen etal. Research on optimal particle swarm Optimization for Multi-objective Task Scheduling in Cloud Computing [J].

Chinese Journal of Electronic Measurement and Instrument,2020, 34(08): 133-143.

[7] Xiaohang Shan,Heng Zhang,Yi Xie. Cloud Workfl ow Scheduling Optimization based on two-dimensional coding Two-stage Coevolutionary Genetic

algorithm [J]. Computer Integrated Manufacturing Systems, 2023, 29(02): 568-580.

[8] Jun Wang,Jiahao Liu,Qiaofeng Song. An on-board edge computing unloading scheme based on Genetic Algorithm [J]. Journal of Nanjing University of

Posts and Telecommunications (Natural Science Edition), 2022, 42(06): 1-9.

[9] Jiangtao Ruan,Haitao Wu,Cheng Qian,Chenhui Huang. Research on cloud computing resource scheduling strategy based on hybrid optimization

algorithm. Computer and Digital Engineering,2020, 48(09): 2220-2224.

[10] Zhiqiang Zhang. Research on multi-source het erogeneous big data algorithm under cloud computing [J]. Computer Knowledge and

Technology,202,18(15):25-27. (in Chinese)




DOI: https://doi.org/10.18686/esta.v10i3.426

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