Online learning-based offloading decision and resource allocation in mobile edge computing-enabled satellite-terrestrial networks

Mobile edge computing (MEC)-enabled satellite-terrestrial networks (STNs) can provide Internet of Things (IoT) devices with global computing services. Sometimes, the network state information is uncertain or unknown. To deal with this situation, we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this paper. The problem of minimizing the average sum task completion delay of all IoT devices over all time periods is formulated. We decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem. A joint optimization scheme of offloading decision and resource allocation is then proposed, which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound (UCB) algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method. Simulation results validate that the proposed scheme performs better than other baseline schemes.