Virtual machine scheduling strategy based on machine learning algorithms for load balancing

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 loa...

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Vydáno v:EURASIP journal on wireless communications and networking Ročník 2019; číslo 1; s. 1 - 16
Hlavní autoři: Sui, Xin, Liu, Dan, Li, Li, Wang, Huan, Yang, Hongwei
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 17.06.2019
Springer Nature B.V
SpringerOpen
Témata:
Adaptive algorithms
Algorithms
Artificial intelligence
Cluster analysis
Clustering
Communications Engineering
Computer centers
Data centers
Differential evolution
Distributed Big Data Processing in SCADA Communication Networks
Energy consumption
Engineering
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Information Systems Applications (incl.Internet)
Load balancing
Machine learning
Migration
Networks
Scheduling
Servers
Signal,Image and Speech Processing
Strategy
Support vector regression
Vector quantization
Virtual environments
Virtual machine
Differential evolution
Support vector regression
Load balancing
Clustering
Virtual machine
ISSN:1687-1499, 1687-1472, 1687-1499
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Shrnutí: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%.
Bibliografie:ObjectType-Article-1
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ISSN:1687-1499
1687-1472
1687-1499
DOI:10.1186/s13638-019-1454-9