GCNCO: Graph Attention-Driven Offloading and Orchestration via Computing and Networking Coordination for Holographic Services

As an important support of next-generation network technologies, edge computing provides crucial support for holographic communication services (HCSs) by offering low latency and high computing power. However, in resource-constrained environments, the deployment of HCSs is significantly affected by the coordinated scheduling of computational and transmission resources, as well as the selection of multi-task processing ratios, which in turn curtails service latency and user experience. For this purpose, this paper proposes an efficient edge network offloading and orchestration framework that jointly optimizes adaptive video processing, multi-task offloading, and resource allocation strategies. Specifically, we model computation offloading and video processing as a stochastic optimization problem to maximize system utility, which is defined as a weighted difference between network satisfaction and user utility. Based on Lyapunov optimization theory, the original long-term optimization problem is decomposed into single time slot subproblem, and further reformulated as a Markov Decision Process (MDP). To effectively solve this problem, we design a Graph attention-driven Computing and Networking Coordination based policy optimization Orchestration algorithm (GCNCO). The multi-dimensional policy joint optimization is achieved by fully sensing the contextual relationship of services and network state distribution. Finally, we compare the proposed algorithm with several benchmark algorithms and validate it using a prototype system. Experimental results demonstrate that GCNCO outperforms existing approaches in terms of convergence speed and performance stability, significantly enhancing system utility and improving the success performance of HCS orchestration.