Bi-objective optimization for multi-task offloading in latency and radio resources constrained mobile edge computing networks

The Mobile Edge Computing (MEC) environment provides leading-edge services to smart mobile devices (SMDs). Besides, computation offloading is a promising service in 5G: it reduces battery drain and applications’ execution time. In this context, we consider a general system consisting of a multi-cell communication network where each base station (BS) is equipped with a MEC server to provide computation offloading services to nearby mobile users. In addition, each SMD handles multiple independent offloadable heavy tasks that are latency-sensitive. The purpose of this article is to jointly optimize tasks’ offloading decisions as well as the allocation of critical radio resources while minimizing the overall energy consumption. Therefore, we have formulated a bi-objective optimization problem that is NP-hard. Because of the short decision time constraint, the optimal solution implementation is infeasible. Accordingly, with the use of the weighted aggregation approach, we propose Intelligent Truncation based Hybrid Local Search (ITHLS) solution. In critical radio resources situations, our solution jointly minimizes the number of penalized SMDs and the overall consumed energy. Finally, simulation experiments were realized to study the ITHLS solution performance compared to some effective state of the art solutions, and the simulation results in terms of decision-making time, energy and number of truncated SMDs are very promising.