Mobile edge computing (MEC) enhances the performance of 5G networks by enabling low-latency, high-speed services through deploying data units of the base station on edge servers located near mobile users. However, determining the optimal capacity of these servers while dynamically offloading tasks and allocating computing resources to meet uncertain user demands presents significant challenges. This paper focuses on the design and planning of edge servers with the dual objectives of minimizing capacity requirements and reducing service latency for 5G services. To handle the complexity of uncertain user demands, we formulate the problem as a two-stage stochastic model, which can be linearized into a mixed-integer linear programming (MILP) problem. We propose a novel approach called accelerated Benders decomposition (ABD) to solve the problem at a large network scale. Numerical experiments demonstrate that ABD achieves the optimal solution of MILP while significantly reducing computation time.
Wu et al. (Mon,) studied this question.