Hastening Co-Inference in Space-Air-Ground Integrated Networks: A Submodular Optimization Based Task Offloading Approach

摘要

Space-Air-Ground Integrated Networks (SAGINs) play a crucial role in the 6G system, offering global coverage and ultra-wide-area broadband access. Meanwhile, advancements in artificial intelligence (AI) have led to a significant increase in model inference demands with stringent latency requirements. In this paper, considering the intelligent services in SAGINs, we explore task offloading for the co-inference problem and improve the inference efficiency by performing model splitting, device association, and resource allocation. This problem is solved iteratively by decomposing it into multiple sub-problems. In particular, the bandwidth allocation problem is solved using convex optimization techniques to obtain a closed-form solution, and the model splitting problem is addressed by systematically searching for the optimal split points. Besides, the joint device association and computing resource allocation problem is reformulated into a monotone submodular function without the satellite, and a low-complexity submodular optimization algorithm is proposed. To further utilize the computing power of the satellite, we introduce a resource reallocation algorithm based on the existing optimization results. These sub-problems iterate alternately until the optimization goal converges. The simulation results show that the proposed algorithm can achieve better latency performance in several inference tasks.