Joint offloading and resource allocation based on Lyapunov algorithm in delay-sensitive SAGIN

The integration of mobile edge computing (MEC) and space-air-ground integrated network (SAGIN) can significantly reduce the user's task processing delay, while relieving the data pressure on the core network, so as to meet the performance requirements of computation capability, throughput, and delay brought about by the massive connectivity of SAGIN, which is an important direction for current research on 6G networks. In this study, we focus on optimizing user delay and usage costs within the SAGIN framework, which comprises low Earth orbit (LEO) satellites, high altitude balloons (HAB), and ground users. Specifically, ground users adopt a partial offloading strategy, wherein computational tasks are offloaded via HABs to MEC servers hosted by LEO satellites for processing. To accommodate the highly dynamic nature of the satellite constellation, we formulate an optimization problem aimed at maximizing the long-term time-averaged latency while minimizing costs, and introduce the Lyapunov optimization method to solve it. The simulation results demonstrate that our proposed algorithm can effectively reduce the network latency and user's usage cost, while ensuring system stability.