Abstract: In order to minimize the processing latency and energy consumption for user tasks in Mobile Edge Computing (MEC) and enhance user experience, this paper focuses on the computation offloading problem in a multi-user, multi-MEC server scenario under constraints on computational resources. With the objective of minimizing the weighted sum of user completion time and energy consumption, the problem is tackled by first decoupling it into two sub-problems: offloading decision and computation resource allocation. The Whale Optimization Algorithm is employed to solve the offloading decision problem, enhancing convergence speed by introducing a nonlinear convergence factor and inertial weight. A feedback mechanism is introduced to prevent local optima, yielding offloading decisions with higher probability of feasibility. The resource allocation problem is addressed using the Lagrange multiplier method to obtain the optimal computation resource allocation for each offloading decision. Finally, stable converged solutions are obtained through multiple iterations. Simulation results demonstrate that, compared to other benchmark solutions, the proposed approach reduces the system overhead by up to 44.6%.

Key words: mobile edge computing, compute offloading, resource allocation, whale optimization algorithm