Reward-Oriented Task Offloading Under Limited Edge Server Power for Multiaccess Edge Computing

In multiaccess edge computing (MEC), tasks are offloaded from mobile devices to servers at the edge of the network. This speeds up task processing without incurring the latency required to reach central servers. However, the power used by edge servers is significant and needs to be cost-effective. We propose a scheme in which tasks are offloaded to servers with the aim of maximizing a reward within a limited power budget, server processing capacities, and wireless network coverage. Our algorithm determines the maximum utilization of each server while favoring the offloading of tasks with high ratios of reward to power requirement. We model the task allocation problem using a minimum-cost-maximum-flow graph, and propose two edge allocation algorithms, one of which is extended to allow task splitting, which offload tasks subject to server capacity by searching for the highest reward. In simulations, our scheme achieved between 8% and 80% higher rewards than alternative schemes, under the same power constraints.