Group Sparse Space Information Network With Joint Virtual Network Function Deployment and Maximum Flow Routing Strategy
Huiting Yang, Wei Liu, Xiangfeng Wang, Jiandong Li
Abstract
For the space information network (SIN) with network function virtualization (NFV), a large number of active nodes deployed with virtual network functions (VNFs) impose heavy coordination overhead. In this paper, we investigate the trade-off between the network maximum flow and coordination overhead under the service function chain (SFC) constraints. Specifically, we propose the group sparse joint VNFs deployment and flow routing strategy (GS-VNF-R) to strike the trade-off between the network maximum flow and coordination overhead. Although the GS-VNF-R scheme can be formulated as a convex problem, for a large-scale SIN, solving the GS-VNF-R problem by traditional convex optimizations imposes a heavy computation burden. In order to reduce the time complexity, we propose a novel optimal low-complexity block-successive upper-bound minimization method of multipliers based group sparse (BSUM-M-GS) algorithm, which can converge to the global optimal with much less complexity. Simulation results show that for some scenarios, 60% of active nodes can be saved by using the proposed GS-VNF-R scheme without any performance loss compared to the full cooperation scheme, which results in significant cooperation overhead reduction. Moreover, simulation results demonstrate that our proposed BSUM-M-GS algorithm can significantly reduce the complexity to the extent of 7 orders of magnitude for some scenarios.