Low-Energy Edge Computing Resource Deployment Algorithm Based on Particle Swarme
In the edge computing environment, in order to reduce the energy consumption of the entire network on the premise of meeting user needs, this paper proposes a low-energy edge computing resource deployment algorithm based on Particle Swarm. First, an edge
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Abstract. In the edge computing environment, in order to reduce the energy consumption of the entire network on the premise of meeting user needs, this paper proposes a low-energy edge computing resource deployment algorithm based on Particle Swarm. First, an edge computing service model based on SDN is designed, which includes three types of devices and three types of management processes. Secondly, the two requirements of user content service request and calculation service request are analyzed, and three energy consumption models of the entire network are constructed: storage energy consumption, calculation energy consumption, and transmission energy consumption. Finally, based on the main idea of the particle swarm optimization algorithm, the solution method is modeled, and a low-energy edge computing resource deployment algorithm based on particle swarm optimization is proposed. In the experimental part, the algorithm in this paper is compared with the traditional algorithm in different service request arrival rate environments. It is verified that the algorithm in this paper reduces the number of storage nodes and computing nodes and saves energy consumption on the entire network. Keywords: Edge computing Resource allocation optimization Energy consumption
Particle swarm
1 Introduction In the 5G network environment, in order to reduce the delay of user services, edge computing technology has been successfully applied to solve this problem. With the growth of user services, the demand for edge computing resources is increasing. How to reduce the energy consumption of the entire network as much as possible under the premise of meeting user needs has become an urgent problem to be solved [1, 2]. Existing researches mainly use methods such as virtual machine migration, game theory and optimization theory to solve the problems of low business execution efficiency and energy consumption reduction. For example, literature [3] propose a sustainable service plan for fog server nodes to improve the efficiency of user task execution, which reduced the energy consumption of fog nodes and the execution time of user tasks. Literature [4] In order to solve the server performance in edge computing © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Q. Liu et al. (Eds.): CENet 2020, AISC 1274, pp. 1557–1564, 2021. https://doi.org/10.1007/978-981-15-8462-6_178
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cannot meet the needs of high response speed and low latency services in the manufacturing industry, based on the rapid migration of virtual machines and network traffic control technology, a business response model with high speed transmission and fast processing is presented, which better solves the problem of low performance of edge computing servers. Literature [5] takes the user task delay requirement as a constraint, combines the task calculation and communication modeling, and proposes a scheduling mechanism that minimizes the user task delay, which reduces the user task
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