Energy Optimization in Multisatellite-Enabled Edge Computing Systems

Edge computing is an efficient way to offload computational tasks for user equipment (UE) which has computation-intensive and latency-sensitive tasks in certain applications. However, UEs cannot offload to ground edge servers when they are in remote areas. Mounting edge servers on low-Earth orbit (LEO) satellites can provide remote UEs with task offloading when the ground infrastructure is not available. In this article, we introduce a multisatellite-enabled edge computing system for offloading UEs’ computational tasks with the aim of minimizing system energy consumption by optimizing users’ association, power control, task scheduling, and computing resource allocation. Specifically, a UE’s partial task is executed locally and the rest of its task is offloaded to a satellite for processing. Such energy minimization problem is formulated as a mixed-integer nonlinear programming (MINLP) optimization problem. By decomposing the original problem into four subproblems, we solve each subproblem with convex optimization methods. In addition, an iterative algorithm is proposed to jointly optimize the task offloading and resource allocation strategy, which achieves a near-optimal solution through several iterations. Finally, the complexity and convergence of the algorithm are verified. In our simulation results, the proposed algorithm is compared with different task offloading and resource allocation schemes in terms of system energy consumption, where 43% energy is saved.