A Strategic Metaheuristic Edge Server Placement Scheme for Energy Saving in Smart City

Abstract

In computing network architecture, the edge server’s candid positioning and server workload are vital in providing quality services, a strategic strategy is necessary to place the edge server in the optimal position. The edge server’s energy consumption and workload balancing can be optimized by the dynamic candid approach based on time constraints. In this work, we place the edge server based on busy and non-busy hours. Shanghai telecom data is used in the research work and shows a strategic plan to save energy consumption by 31.97% during busy and non-busy hours. It also increases the workload balance up to 7.19% of the edge server, and the 16.16 % increases the utilization rate of the edge server by using three different well-known Particle swarm optimization (PSO), Top-First, and Random algorithms.