A many‐objective optimization based intelligent algorithm for virtual machine migration in mobile edge computing

Summary With the rapid development of big data, the explosive growth of data promotes the progress of the Internet of Things (IoT). Because it is hard for traditional cloud computing to meet vast computing tasks, scholars propose mobile edge computing (MEC) for the IoT. However, the mobility of user...

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Vydané v:Concurrency and computation Ročník 35; číslo 23
Hlavní autori: Fan, Tian, Guo, Wanwan, Zhang, Zhixia, Cui, Zhihua
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Hoboken, USA John Wiley & Sons, Inc 25.10.2023
Wiley Subscription Services, Inc
Predmet:
Algorithms
Big Data
Cloud computing
Costs
Edge computing
Energy consumption
Evolutionary algorithms
Internet of Things
many‐objective evolutionary algorithm
many‐objective optimization
Mobile computing
mobile edge computing
Optimization
Virtual environments
virtual machine migration
ISSN:1532-0626, 1532-0634
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Shrnutí:Summary With the rapid development of big data, the explosive growth of data promotes the progress of the Internet of Things (IoT). Because it is hard for traditional cloud computing to meet vast computing tasks, scholars propose mobile edge computing (MEC) for the IoT. However, the mobility of users results in the instability of MEC performance. Besides, the conflict of interest between users and service providers needs to be balanced. To solve these problems, this paper constructs a virtual machine migration model based on many‐objective optimization (MaOVMMM). In MaOVMMM, four objectives are considered simultaneously: communication expense, computing expense, delay, and energy consumption. A many‐objective evolutionary algorithm with double population confrontation (MaOEA‐DPC) is suggested to support the MaOVMMM that is proposed. First, the population confrontation strategy is designed to better simulate the relationship between users and service providers. Second, the dynamic probability integration selection strategy is used to ensure the evolution ability of the algorithm. Simulation results demonstrate the effectiveness and superiority of MaOEA‐DPC when compared with other algorithms. This proposed approach can provide a superior virtual machine migration scheme for decision‐makers.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.7770