期刊
EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING
卷 -, 期 -, 页码 -出版社
SPRINGER
DOI: 10.1186/s13638-019-1454-9
关键词
Load balancing; Virtual machine; Support vector regression; Clustering; Differential evolution
资金
- Science and Technology project of 13th Five-Year planning of the Education Department of Jilin Province [JJKH20180750KJ, JJKH20190593KJ]
- Key Science and Technology Project of Jilin Province [20160204019GX]
- CERNET Innovation Project [NGII20180127]
With the rapid increase of user access, load balancing in cloud data center has become an important factor affecting cluster stability. From the point of view of green scheduling, this paper proposed a virtual machine intelligent scheduling strategy based on machine learning algorithm to achieve load balancing of cloud data center. Firstly, a load forecasting algorithm based on genetic algorithm (SVR_GA), k-means clustering algorithm based on optimized min-max, and adaptive differential evolution algorithm (ESA_DE) to enhance local search ability are proposed to solve the load imbalance problem in cloud data center. The experimental results showed that compared with other classical algorithms, the proposed virtual machine scheduling strategy reduces the number of virtual machine migration by 94.5% and the energy consumption of cloud data center by 49.13%.
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