A Study on Computation Offloading in MEC Systems using Whale Optimization Algorithm

In recent years, computation offloading in multi-access edge computing (MEC) has received a lot of attention. For example, the joint optimization of computing resource allocation and offloading decision was studied to reduce the overall latency and energy consumption of MEC offloading. Such problem of optimizing the computing resource allocation and offloading decision is often formulated as a mixed-integer nonlinear programming (MINLP) problem, which is also NP-hard in general. Various solution approaches have been proposed such as heuristics, dynamic programming, branch-and-bound, and machine learning. Motivated by applications to a variety of complex optimization problems, in this paper, we provide an alternative meta-heuristic method using whale optimization algorithm (WOA). We have proposed a joint computing resource allocation and offloading decision system using the WOA to minimize the total computing overhead of mobile users (MUs), including completion time and energy consumption. Through numerical simulations, our proposed algorithm is more efficient than several baseline schemes for reducing the total computing overhead of MUs.