Multimodal Optimization of Edge Server Placement Considering System Response Time

Abstract

Mobile edge computing (MEC) deploys computing and storage resources close to mobile devices, enabling resource demanding applications to run on mobile devices with short network latency. In the past few years, large numbers of research works focused on the research hotspots in MEC, such as computation offloading and energy efficiency. However, few researchers have investigated the deployment of edge servers. On the one hand, blindly deploying numerous edge servers will result in a large amount of capital expenditure. On the other hand, the deployment of edge servers is a multimodal problem that should provide decision makers with multiple deployment options to deal with the impact of unmeasured real-world factors. Considering these factors, we study the multimodal optimization problem of edge server placement (MESP) with the goal of minimizing the average system response time in this work. Regarding the difficulty of the MESP problem, we propose a heuristic algorithm that combines particle swarm optimization and niching technology to obtain a set of competitive placement solutions. Extensive experiments over a real-world dataset show that the proposed algorithm can significantly reduce the system response time.

A Hybrid metaheuristic Algorithm for Edge Site Deployment with User Coverage Maximization and Cost Minimization

“…Given the edge site deployment solution, edge server placement is to decide which sites edge servers (resources) are placed to maximize the overall performance with restricted edge resources. Zhang et al [23] proposed a niche PSO to minimize the overall respond time considering the placement as a multimodal optimization problem, which divides similar individuals into a niche during the population upgrade. Li et al [24] and Zhang et al [25] employed K-means++ to cluster edge sites into k classes, and deploy k edge servers on the sites closest to theses classes' centres.…”