SFC-Based Service Provisioning for Reconfigurable Space-Air-Ground Integrated Networks

• Published in: IEEE journal on selected areas in communications Vol. 38; no. 7; pp. 1478 - 1489
• Main Authors: Wang, Guangchao, Zhou, Sheng, Zhang, Shan, Niu, Zhisheng, Shen, Xuemin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computation; Computer simulation; Delays; Earth; Greedy algorithms; heterogeneous networks; Linear programming; Monitoring; Nonlinear programming; Planning; Provisioning; Quality of service; Reconfiguration; Resource management; Resource utilization; Satellites; Service function chaining (SFC); space-air-ground integrated networks (SAGIN); virtual network functions (VNFs); Virtual networks; Wireless networks
• ISSN: 0733-8716; 1558-0008
• DOI: 10.1109/JSAC.2020.2986851

Space-air-ground integrated networks (SAGIN) extend the capability of wireless networks and will be the essential building block for many advanced applications, like autonomous driving, earth monitoring, and etc. However, coordinating heterogeneous physical resources is very challenging in such a large-scale dynamic network. In this paper, we propose a reconfigurable service provisioning framework based on service function chaining (SFC) for SAGIN. In SFC, the network functions are virtualized and the service data needs to flow through specific network functions in a predefined sequence. The inherent issue is how to plan the service function chains over large-scale heterogeneous networks, subject to the resource limitations of both communication and computation. Specifically, we must jointly consider the virtual network functions (VNFs) embedding and service data routing. We formulate the SFC planning problem as an integer non-linear programming problem, which is NP-hard. Then, a heuristic greedy algorithm is proposed, which concentrates on leveraging different features of aerial and ground nodes and balancing the resource consumptions. Furthermore, a new metric, aggregation ratio (AR) is proposed to elaborate the communication-computation tradeoff. Extensive simulations shows that our proposed algorithm achieves near-optimal performance. We also find that the SAGIN significantly reduces the service blockage probability and improves the efficiency of resource utilization. Finally, a case study on multiple intersection traffic scheduling is provided to demonstrate the effectiveness of our proposed SFC-based service provisioning framework.