Affiliations: [a] School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China
| [b] School of Entrepreneurship, Wuhan University of Technology, Wuhan, China
| [c] School of Information Engineering, Wuhan University of Technology, Wuhan, China
Correspondence:
[*]
Corresponding authors: Bing Tang, School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China. E-mails: btang@hnust.edu.cn (Bing Tang) and zjiaming@whut.edu.cn. (Jiaming Zhang)
Abstract: The rapid development of the Internet of Things and 5G networks have generated a large amount of data. By offloading computing tasks from mobile devices to edge servers with sufficient computing resources, network congestion and data transmission delays can be effectively reduced. The placement of edge server is the core of task offloading and is a multi-objective optimization problem with multiple resource constraints. Efficient placement approach can effectively meet the needs of mobile users to access services with low latency and high bandwidth. To this end, an optimization model of edge server placement has been established in this paper through minimizing both communication delay and load difference as the optimization goal. Then, an Edge Server placement based on meta-Heuristic alGorithM (ESH-GM) has been proposed to achieve multi-objective optimization. Firstly, the K-means algorithm is combined with the ant colony algorithm, and the pheromone feedback mechanism is introduced into the placement of edge servers by emulating the mechanism of ant colony sharing pheromone in the foraging process, and the ant colony algorithm is improved by setting the taboo table to improve the convergence speed of the algorithm. Then, the improved heuristic algorithm is used to solve the optimal placement of edge servers. Experimental results using Shanghai Telecom’s real datasets show that the proposed ESH-GM achieves an optimal balance between low latency and load balancing, while guaranteeing quality of service, which outperforms several existing representative approaches.
Keywords: Mobile edge computing, server placement, heuristic algorithm, performance
optimization
