Journal of System Simulation
Abstract
Abstract: Focusing on the low efficiency of cloud job scheduling and the insufficient utility of resource, a job scheduling algorithm based on Hopfield Neural Network is proposed. In order to improve the resource scheduling ability of the system, The resource characteristics which influence the cloud job scheduling are shown. The mathematical model of resource constraints is established, and the Hopfield energy function is designed and optimized. The average utilization rate of 9 nodes is analyzed by using the standard test cases, and the performance and resource utilization of the proposed strategy are compared with three typical algorithms. The results show that the average efficiency of the cloud job scheduling based on the algorithm is improved significantly.
Recommended Citation
Guo, Yudong and Zuo, Jinping
(2019)
"The Scheduling Algorithm of Cloud Job Based on Hopfield Neural Network,"
Journal of System Simulation: Vol. 31:
Iss.
12, Article 36.
DOI: 10.16182/j.issn1004731x.joss.19-FZ0323
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol31/iss12/36
First Page
2859
Revised Date
2019-07-15
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.19-FZ0323
Last Page
2867
CLC
TP391.9
Recommended Citation
Guo Yudong, Zuo Jinping. The Scheduling Algorithm of Cloud Job Based on Hopfield Neural Network[J]. Journal of System Simulation, 2019, 31(12): 2859-2867.
DOI
10.16182/j.issn1004731x.joss.19-FZ0323
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