Virtual machine scheduling strategy based on machine learning algorithms for load balancing
Abstract 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 ac...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2019-06-01
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| Series: | EURASIP Journal on Wireless Communications and Networking |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13638-019-1454-9 |
| _version_ | 1818852114506448896 |
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| author | Xin Sui Dan Liu Li Li Huan Wang Hongwei Yang |
| author_facet | Xin Sui Dan Liu Li Li Huan Wang Hongwei Yang |
| author_sort | Xin Sui |
| collection | DOAJ |
| description | Abstract 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%. |
| first_indexed | 2024-12-19T07:15:46Z |
| format | Article |
| id | doaj.art-53e52f90c03e42b99e3b95b62f8f48f1 |
| institution | Directory Open Access Journal |
| issn | 1687-1499 |
| language | English |
| last_indexed | 2024-12-19T07:15:46Z |
| publishDate | 2019-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Wireless Communications and Networking |
| spelling | doaj.art-53e52f90c03e42b99e3b95b62f8f48f12022-12-21T20:31:04ZengSpringerOpenEURASIP Journal on Wireless Communications and Networking1687-14992019-06-012019111610.1186/s13638-019-1454-9Virtual machine scheduling strategy based on machine learning algorithms for load balancingXin Sui0Dan Liu1Li Li2Huan Wang3Hongwei Yang4College of Computer Science and Technology, Changchun University of Science and TechnologyCollege of Computer Science and Technology, Changchun University of Science and TechnologyCollege of Computer Science and Technology, Changchun University of Science and TechnologyCollege of Computer Science and Technology, Changchun University of Science and TechnologyCollege of Computer Science and Technology, Changchun University of Science and TechnologyAbstract 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%.http://link.springer.com/article/10.1186/s13638-019-1454-9Load balancingVirtual machineSupport vector regressionClusteringDifferential evolution |
| spellingShingle | Xin Sui Dan Liu Li Li Huan Wang Hongwei Yang Virtual machine scheduling strategy based on machine learning algorithms for load balancing EURASIP Journal on Wireless Communications and Networking Load balancing Virtual machine Support vector regression Clustering Differential evolution |
| title | Virtual machine scheduling strategy based on machine learning algorithms for load balancing |
| title_full | Virtual machine scheduling strategy based on machine learning algorithms for load balancing |
| title_fullStr | Virtual machine scheduling strategy based on machine learning algorithms for load balancing |
| title_full_unstemmed | Virtual machine scheduling strategy based on machine learning algorithms for load balancing |
| title_short | Virtual machine scheduling strategy based on machine learning algorithms for load balancing |
| title_sort | virtual machine scheduling strategy based on machine learning algorithms for load balancing |
| topic | Load balancing Virtual machine Support vector regression Clustering Differential evolution |
| url | http://link.springer.com/article/10.1186/s13638-019-1454-9 |
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