Bulk Transfers With GCN Scheduling In Digital Twin Networks

Digital Twin Network (DTN) constructs a many-to-many mapping network by communicating and collaborating with massive Digital Twins (DTs), which can better assist the management and operation of large-scale modern systems. However, the emergence of DTN mirrors a growing demand for bulk data transfer between geo-distributed DT nodes in the physical network infrastructure. Conventional end-to-end connections are facing a great challenge in the presence of the background traffic fluctuation. In this paper, we propose a graph convolutional network (GCN)-enabled scheduling method to schedule bulk data transfers across the DTN in a store-and-forward (SnF) manner. Instead of solving the complex SnF scheduling problem on the entire network, the proposed method decomposes the problem into multiple sub-problems on different pre-computed routes. Instead of learning the scheduling results directly, the GCN model is used to predict the reachability of DT nodes. These unreachable nodes will be excluded from the scheduling process. Our studies show that the proposed method obtains better network performance and lower complexity when compared with the conventional scheduling methods.