Joint Task Offloading and Radio Resource Management in Stochastic MEC Systems

In this paper, we present a novel coexistence uplink-downlink stochastic mobile edge computing (MEC) system that considers the dynamic characteristics of both small cell base stations (SBSs) and user equipments (UEs). To devise an efficient radio resource management strategy encompassing user association, channel allocation, and power allocation, we formulate an optimization problem that considers time, energy, and achievable rate in the utility function. The formulated problem is a Mixed Integer Nonlinear Program (MINLP) and has been proven to be NP-hard. To address this complexity, we propose a unified nature-inspired optimization framework, which can be deployed for subproblems in various settings and can be integrated with the Whale Optimization Algorithm (WOA), Improved Whale Optimization Algorithm (IWOA), and Particle Swarm Optimization (PSO). Through our rigorous mathematical and numerical analysis, the proposed algorithms show that they can converge to a near-optimal solution while keeping negligible optimality gaps. Our numerical results show the advantages and drawbacks of the proposed algorithms, highlighting their potential for effective resource management in MEC systems. The results also show the performance evaluation of stochastic characteristics on the performance of MEC.