Energy and Computational Resources Optimization in a Mobile Edge Computing Node

Mobile edge computing most of time deals with constrained mobile devices. Especially for their limited processing capacity and available battery power, these devices must offload a part of their heavy tasks that require a lot of computation and are energy consuming. This choice remains the only option in some circumstance, especially when the battery drains off. Besides, the local CPU frequency allocated to processing has a huge impact on devices energy consumption. Additionally, when mobile devices handle many tasks, the decision of the part to offload becomes critical. Actually, it must consider the wireless network state, the available processing resources at both sides, and particularly the local available battery power. In this paper, we consider a single mobile device that is energy constrained and equipped with a list of heavy offloadable tasks that are delay constrained. Therefore, we formulated the corresponding optimization problem, and proposed a Simulated Annealing based heuristic solution scheme. In order to evaluate and compare this solution, we carried out a set of simulation experiments. Finally, the obtained results in terms of energy are very encouraging. In addition, our solution performs the offloading decisions within an acceptable and feasible timeframes.