Graph-Deep-Reinforcement-Learning-Based Joint Computation Offloading and SFC Deployment in UAV-Assisted Edge Computing

As unmanned aerial vehicles (UAV) can provide edge service flexibility, the UAV-assisted edge computing (UAEC) has attracted much attention recently. Computation offloading and service function chain (SFC) deployment are closely related and interactional, which can influence the efficient usage of limited resources in UAEC. Therefore, a joint computation offloading and SFC deployment (JCOSD) scheme is researched in this paper. The offloaded tasks will be processed by the ordered virtual network functions (VNF) in SFC, and the VNF can be deployed on UAV. The delay and energy consumption are minimized at the same time. The constraints on task requests and computing capabilities of heterogeneous nodes are considered. Considering the complex network features and the relationship between heterogeneous nodes in UAEC, a graph neural network (GNN)-based algorithm is proposed to extract network node features based on local observations. The edge weights including bandwidth, expected delay, and expected energy consumption are applied to effectively reflect the node importance. Then the global feature embeddings from GNN are used as the state input of a multi-head double deep Q network. Computation offloading and SFC deployment strategies are jointly determined. The numerical results have shown the effectiveness of the proposed JCOSD scheme in different cases, and JCOSD can achieve up to 22.51%, 47.10%, 56.69%, and 42.29% better performance than other four benchmarks.