Online Computation Offloading for Collaborative Space/Aerial-Aided Edge Computing Toward 6G System
Yi Liu, Li Jiang, Qi Qi, Kan Xie, Shengli Xie
Abstract
In 6G systems, space-air-ground integrated network (SAGIN), relying on space/aerial communications to complement terrestrial networks, is developed to achieve worldwide connectivity and multi-service access especially in remote areas. However, the heavy workload of the resource-limited satellites may raise an important issue about reducing service coverage and quality. In this article, we propose a collaborative edge computing framework for SAGIN-aided 6G system, in which the LEO satellites are considered as both “servers” and “users”. Excepting provide services for ground users/devices, the LEO satellites is able to offload the tasks to nearby aircrafts via one-hop link or offload them to the cloud server along multi-hop satellite path. To minimize the long-term task completion delay of LEO satellites, a stochastic optimization problem is formulated by considering the variation of the space/aerial environment. The Lyapunov-based optimization method is developed to solve the problem and the delayed online learning technique is adapted to predict dynamic task arrival and queue length of satellites and aircrafts. Numerical results confirm the effectiveness of the proposed collaborative offloading scheme for reducing tasks completion delay of LEO satellites while guaranteeing computation efficiency.