An Efficient and Low-Delay SFC Recovery Method in the Space–Air–Ground Integrated Aviation Information Network with Integrated UAVs

Abstract

Unmanned aerial vehicles (UAVs), owing to their flexible coverage expansion and dynamic adjustment capabilities, hold significant application potential across various fields. With the emergence of urban low-altitude air traffic dominated by UAVs, the integrated aviation information network combining UAVs and manned aircraft has evolved into a complex space–air–ground integrated Internet of Things (IoT) system. The application of 5G/6G network technologies, such as cloud computing, network function virtualization (NFV), and edge computing, has enhanced the flexibility of air traffic services based on service function chains (SFCs), while simultaneously expanding the network attack surface. Compared to traditional networks, the aviation information network integrating UAVs exhibits greater heterogeneity and demands higher service reliability. To address the failure issues of SFCs under attack, this study proposes an efficient SFC recovery method for recovery rate optimization (ERRRO) based on virtual network functions (VNFs) migration technology. The method first determines the recovery order of failed SFCs according to their recovery costs, prioritizing the restoration of SFCs with the lowest costs. Next, the migration priorities of the failed VNFs are ranked based on their neighborhood certainty, with the VNFs exhibiting the highest neighborhood certainty being migrated first. Finally, the destination nodes for migrating the failed VNFs are determined by comprehensively considering attributes such as the instantiated SFC paths, delay of physical platforms, and residual resources. Experiments demonstrate that the ERRRO performs well under networks with varying resource redundancy and different types of attacks. Compared to methods reported in the literature, the ERRRO achieves superior performance in terms of the SFC recovery rate and delay.