Multi-objective edge server placement using the whale optimization algorithm and game theory

Due to the users’ mobility, new online applications, as well as low processing power and limited energy of smart devices, traditional cloud computing models could not provide new required services. Cloud service providers improve the quality of their services by moving some servers to the edge of th...

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Vydané v:Soft computing (Berlin, Germany) Ročník 27; číslo 21; s. 16143 - 16157
Hlavní autori: Asghari, Ali, Azgomi, Hossein, darvishmofarahi, Zahra
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2023
Springer Nature B.V
Predmet:
Algorithms
Application of Soft Computing
Artificial Intelligence
Cloud computing
Clustering
Computational Intelligence
Control
Costs
Distance learning
Edge computing
Energy consumption
Engineering
Game theory
Genetic algorithms
Literature reviews
Mathematical Logic and Foundations
Mechatronics
Methods
Network latency
Neural networks
Optimization
Placement
Quality of service architectures
Robotics
Servers
Workloads
Edge server placement
WOA algorithm
Load balancing
Game theory
Latency
ISSN:1432-7643, 1433-7479
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Shrnutí:Due to the users’ mobility, new online applications, as well as low processing power and limited energy of smart devices, traditional cloud computing models could not provide new required services. Cloud service providers improve the quality of their services by moving some servers to the edge of the network and closer to mobile users. Considering the moving nature of users and the heterogeneous service demands in different areas, the optimal placement of servers plays an important role in increasing the quality of service provided to users. However, because of the large number of servers, finding the optimal location of these resources is a serious challenge. In the proposed method of this paper (MES-WG), in the first step, the geographical area of server deployment is divided into smaller sub-regions to reduce the complexity of the problem. Then, by using the WOA algorithm the search agent finds the optimal location of the servers. In the next step, a neural network is used for the local placement of all servers in each area. Finally, game theory is deployed for the convergence of resource placement in all sub-regions. The experimental results show that the proposed method reduces the network latency by 33.5% and also improves the load balance on servers by 28.2%, compared to some of the state-of-the-art methods.
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-023-07995-3