Abstract: At present,the traditional cloud computing mode can not meet the needs of users in low latency scenarios,so mobile edge computing comes into being.In order to make the edge servers placed in the same area have lower total energy consumption and balanced workload,and an ant colony optimization energy consumption load balancing placement algorithm ACO-ELP(ant colony optimization energy-consumption load-balancing placement) for energy consumption optimization and load balancing is proposed.Firstly,by constructing the power consumption model and load balancing model,the problem is defined,and the actual parameters are matched with the algorithm variables.In the iterative process,the ant colony algorithm is optimized.By dynamically controlling the volatilization and retention rate of pheromone,the iterative speed of the algorithm is accelerated,and the maximum and minimum value of pheromone is controlled to ensure that the algorithm can search the global optimal solution as much as possible and will not fall into the local optimal solution.Finally,the algorithm is simulated and evaluated with the data of Telecom base stations in Shanghai.The results show that compared with the basic placement algorithm,the algorithm not only reduces the number of servers and energy consumption,but also significantly reduces the load deviation.

Key words: Edge computing, Server placement, Ant colony algorithm, Energy consumption, Load balancing