Cost-Aware Dynamic SFC Mapping and Scheduling in SDN/NFV-Enabled Space–Air–Ground-Integrated Networks for Internet of Vehicles
Junling Li, Weisen Shi, Huaqing Wu, Shan Zhang, Xuemin Shen
Abstract
Space–air–ground-integrated networks (SAGINs) are deemed as a promising solution to support multifarious Internet of Vehicles (IoV) services with diversified Quality-of-Service (QoS) requirements in future communication networks. Network function virtualization (NFV) and software-defined networking (SDN) are two complementary and promising technologies to reduce the function provisioning cost and coordinate the heterogeneous physical resources in SAGIN. In this article, we investigate the online dynamic virtual network function (VNF) mapping and scheduling in SAGIN, considering the dynamicity of IoV services. The VNF live migration, VNF reinstantiation, and VNF rescheduling are enabled to increase the service acceptance ratio and service provider’s profits. Considering the heterogeneity of space, air, and ground nodes, we first model the migration cost and additional delay incurred by VNF live migration and reinstantiation. We then formulate the dynamic VNF mapping and scheduling jointly as a mixed-integer linear programming (MILP) problem with specified cost and delay models. We propose two Tabu search (TS)-based algorithms, i.e., TS-based VNF remapping and rescheduling (TS-MAPSCH) algorithm and TS-based pure VNF rescheduling (TS-PSCH) algorithm, to obtain suboptimal solutions to the MILP problem efficiently. Simulation results show that the proposed solution is very close to the optimum and that the proposed dynamic algorithms outperform existing works with respect to multiple performance metrics, including the service provider’s profit, service acceptance ratio, and QoS satisfaction level.