A Game-Based Joint Task Offloading and Computation Resource Allocation Strategy for Hybrid Edgy-Cloud and Cloudy-Edge Enabled LEO Satellite Networks

Low earth orbit (LEO) satellite based multi-access edge computing is an efficient approach to meet the ever-increasing computation demands in extensive coverage. More-over, two kinds of tasks, namely edgy-cloud task and cloudy-edge task, are developed for distinct edge computing applications. In this paper, a joint task offloading and computation resource allocation strategy is proposed for the hybrid edgy-cloud and cloudy-edge enabled LEO satellite networks. Specifically, each satellite is allowed to bear both edgy-cloud and cloudy-edge tasks. To realize the efficient utilization of limited resources, we design the cost functions for the two kinds of tasks, which take the delay, energy consumption, and monetary cost for resource usage into consideration. Then, a cost minimization problem is formulated for each terminal and a multi-player non-cooperative potential game is further constructed to characterize the interactions among different terminals where the existence of the Nash equilibrium (NE) is proved. We also design a hybrid particle swarm optimization based algorithm to achieve the NE.