Towards Multi-Criteria Heuristic Optimization for Computational Offloading in Multi-Access Edge Computing

In recent years, there has been considerable interest in computational offloading algorithms. The interest is mainly driven by the potential savings that offloading offers in task completion time and mobile device energy consumption. This paper builds on authors' previous work on computational offloading and describes a multi-objective optimization model that optimizes time and energy in a network with multiple Multi-Access Edge Computing servers (MECs) and Mobile Devices (MDs). Each MD has multiple computational jobs to process, and each task can be processed locally or offloaded to one of the MEC servers. Several heuristic offloading policies are proposed and tested with an objective function with a range of weightings for optimizing time and energy. The approaches are illustrated with the help of three test cases of varying complexity. The objective function shows a continuous variation as the emphasis is placed on either time or energy saving by the weighting factors. The numerical tests demonstrate that the proposed heuristic algorithms produce near-optimal computational offloading solutions while considering a combined weighted score for schedule task completion time and energy.