Abstract: The rapid development of the Internet-of-Things and 5G networks generates a large amount of data.By offloading computing tasks from mobile devices to edge servers with sufficient computing resources,network congestion and data propagation delays can be effectively reduced.The placement of edge server is the core of task offloading,and efficient placement method can effectively satisfy the needs of mobile users to access services with low latency and high bandwidth.To this end,an optimization model of edge server placement is established through minimizing both access delay and load difference as the optimization goal.Then,based on the heuristic algorithm,a mobile edge server placement method called ESPHA (Edge Server Placement Method Based on Heuristic Algorithm) is proposed to achieve multi-objective optimization.Firstly,the K-means algorithm is combined with the ant colony algorithm,the pheromone feedback mechanism is introduced into the placement method 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.Finally,the improved heuristic algorithm is used to solve the optimal placement.Experiments using Shanghai Telecom's real datasets show that the proposed method achieves an optimal balance between low latency and load balancing under the premise of guaranteeing quality of service,and outperforms several existing representative methods.

Key words: Access delay, Edge server placement, Heuristic algorithm, Mobile edge computing, Workload balancing