Edge server placement and allocation optimization: a tradeoff for enhanced performance

Considering the expansion of the Internet of Things (IoT) and the volume of data and user requests, Mobile Edge Computing (MEC) is considered a novel and efficient solution that puts decentralized servers at the network&rsquos edge. This has the effect of lowering bandwidth demand and transmission latency. Optimal edge server placement and allocation, as the first stage of MEC, can improve end-user service quality, edge computing system utility, and cost and energy consumption. The majority of previous edge server placement studies have employed only one objective or developed a fitness function by the weighted sum method for optimization. Usually, using a single optimization objective without considering other objectives cannot yield the desired results for a problem with a multi-objective design. On the other hand, assigning weights to objectives can lead to losing optimal points in non-convex problems and selecting improper weights. Therefore, in this paper, we propose a multi-objective solution for the positioning and allocation of edge servers for MEC services based on the NSGA-II algorithm. In this regard, we identify two workload variance and latency reduction objectives with extensive evaluations. The experimental evaluation of the results using real-world data reveals that solutions based on the NSGA-II yield superior convergence and diversity of Pareto front points compared to Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Biogeography Based Optimization (MOBBO), and Adaptive Weighted Sum Method (AWSM). Additionally, it effectively mitigates workload variance on servers and exhibits an average latency reduction of 8.79% in comparison to the adaptive weighted-sum approach, 9.19% in comparison to MOPSO, and 0.28% in comparison to MOBBO.