Maximizing Profit With Energy-Efficient 5G Edge Orchestration

The rapid development of 5G brought advanced capabilities to support low-latency and bandwidth-hungry applications, including edge computing capabilities. However, service orchestration on the network edge is characterized by the scarcity of available resources in the edge infrastructure, emphasizing the need for optimal resource allocation. In this work, we explore a multi-objective optimization approach to resource allocation in 5G edge networks that focuses on the deployment of edge applications and User Plane Functions (UPFs) to maximize operator’s profit with minimum energy consumption. Due to the computationally expensive nature of the optimization formulation, we develop a solution that relies on the use of genetic algorithms, specifically a non-dominated sorting genetic algorithm (NSGA-II)-based multi-objective approach. We compare the performance obtained with our approach against the optimal solution, where the latter can be obtained only for small instances, and another benchmark heuristic. We do this for three different topologies under varying demand levels. Results show that our approach yields solid performance, outperforming the benchmark by at least 25%.