Service Chaining Placement Based on Satellite Mission Planning in Ground Station Networks

TL;DR

This paper addresses the challenge of VNF placement and traffic routing in satellite ground station networks, proposing location-aware algorithms that optimize resource utilization considering satellite mission planning.

Contribution

It introduces a novel resource allocation model and algorithms tailored for satellite ground stations, integrating satellite orbit and mission data for improved VNF deployment.

Findings

LARA algorithms reduce resource utilization compared to non-predictive methods.

Simulation demonstrates effectiveness across Fat-Tree, BCube, and VL2 network topologies.

Proposed approach enhances efficiency in satellite ground station service provisioning.

Abstract

As the increase in satellite number and variety, satellite ground stations should be required to offer user services in a flexible and efficient manner. Network function virtualization (NFV) can provide a new paradigm to allocate network resources on-demand for user services over the underlying network. However, most of the existing work focuses on the virtual network function (VNF) placement and routing traffic problem for enterprise data center networks, the issue needs to further study in satellite communication scenarios. In this paper, we investigate the VNF placement and routing traffic problem in satellite ground station networks. We formulate the problem of resource allocation as an integer nonlinear programming (INLP) model and the objective is to minimize the link resource utilization and the number of servers used. Considering the information about satellite orbit fixation and mission planning, we propose location-aware resource allocation (LARA) algorithms based on Greedy and IBM CPLEX 12.10, respectively. The proposed LARA algorithm can assist in deploying VNFs and routing traffic flows by predicting the running conditions of user services. We evaluate the performance of our proposed LARA algorithm in three networks of Fat-Tree, BCube, and VL2. Simulation results show that our proposed LARA algorithm performs better than that without prediction, and can effectively decrease the average resource utilization of satellite ground station networks.