Heuristic Approaches for Computational Offloading in Multi-Access Edge Computing Networks

Computational offloading is a strategy by which mobile device (MD) users can access the superior processing power of a Multi-Access Edge Computing (MEC) server network. In this paper, we contribute a model of a system that consists of multiple MEC servers and multiple MD users. Each MD has multiple computational tasks to perform, and each task can either be computed locally on the MD, or it can be offloaded to one of the MEC servers. For this system and having global knowledge, we compute the theoretical optimal allocation that minimises the time required to complete the computation of all tasks. Subsequently, we contribute a distributed heuristic algorithm that allows each MD to independently, and using local knowledge only, decide how to handle each individual job. Furthermore, we propose three approaches to decide whether to offload each individual job, and three mechanisms to determine which MEC server each task should be offloaded to. We use simulations to evaluate those approaches in terms of how well they can approximate the theoretical optimum. The proposed heuristic algorithm is tested on a range of experiments, and the results demonstrate that the
heuristic algorithm can produce reasonable quality solutions.