Machine Learning Empowered Green Task Offloading for Mobile Edge Computing in 5G Networks

With the exponential growth of computation- intensive and latency-sensitive applications in 5G, it is hard to satisfy the heterogeneous requirements for increased data traffic with limited on-board resources. Mobile Edge Computing (MEC) has been considered as potential solution to offload computation task from User Equipment's (UEs) to network edge in order to address certain challenges such as intolerable delay, high cost of resource utility in terms of energy and bandwidth. In this paper, we model the task offloading which aims at minimizing the overall energy consumption in task computation and latency requirements from both communication and computation aspect in MEC scenario. We first formulate the task offloading as classification problem while considering energy and latency constraints and then propose novel supervised learning-based classification techniques for classification of task, whether to offload or not, from UE to edge network. The numerical results demonstrate the capability of proposed offloading decision solution set to guarantee Quality of Experience (QoE) and offloading utility in terms of accuracy score for low- and high-energy devices.