An edge server deployment method based on optimal benefit and genetic algorithm

Abstract

Abstract With the speedy advancement and accelerated popularization of 5G networks, the provision and request of services through mobile smart terminals have become a hot topic in the development of mobile service computing. In this scenario, an efficient and reasonable edge server deployment solution can effectively reduce the deployment cost and communication latency of mobile smart terminals, while significantly improving investment efficiency and resource utilization. Focusing on the issue of edge server placement in mobile service computing environment, this paper proposes an edge server deployment method based on optimal benefit quantity and genetic algorithm. This method is firstly, based on a channel selection strategy for optimal communication impact benefits, it calculates the quantity of edge servers which can achieve optimal benefit. Then, the issue of edge server deployment is converted to a dual-objective optimization problem under three constraints to find the best locations to deploy edge servers, according to balancing the workload of edge servers and minimizing the communication delay among clients and edge servers. Finally, the genetic algorithm is utilized to iteratively optimize for finding the optimal resolution of edge server deployment. A series of experiments are performed on the Mobile Communication Base Station Data Set of Shanghai Telecom, and the experimental results verify that beneath the limit of the optimal benefit quantity of edge servers, the proposed method outperforms MIP, K-means, ESPHA, Top-K, and Random in terms of effectively reducing communication delays and balancing workloads.

Keywords

• Mobile service computing
• Optimal benefit quantity
• Load balancing
• Communication delay
• Edge server deployment