Controller Placement in SDN-enabled 5G Satellite-Terrestrial Networks

TL;DR

This paper addresses the optimal placement of SDN controllers in 5G satellite-terrestrial networks to minimize control path failure probability and latency, using MILP modeling and submodular optimization for efficient solutions.

Contribution

It formulates a novel optimization problem for SDN controller placement in satellite-terrestrial networks and proposes an efficient approximation method using submodular optimization.

Findings

The proposed approach effectively reduces control path failure probability.

The submodular optimization method provides near-optimal solutions with low computational complexity.

Simulation results confirm the effectiveness and efficiency of the proposed placement strategy.

Abstract

SDN-enabled Integrated satellite-terrestrial networks (ISTNs), can provide several advantages including global seamless coverage, high reliability, low latency, etc. and can be a key enabler towards next generation networks. To deal with the complexity of the control and management of the integrated network, leveraging the concept of software-defined networking (SDN) will be helpful. In this regard, the SDN controller placement problem in SDN-enabled ISTNs becomes of paramount importance. In this paper, we formulate an optimization problem for the SDN controller placement with the objective of minimizing the average failure probability of SDN control paths to ensure the SDN switches receive the instructions in the most reliable fashion. Simultaneously, we aim at deploying the SDN controllers close to the satellite gateways to ensure the connection between the two layers occurs with the lowest latency. We first model the problem as a mixed integer linear program (MILP). To reduce the time complexity of the MILP model, we use submodular optimization techniques to generate near-optimal solutions in a time-efficient manner. Finally, we verify the effectiveness of our approach by means of simulation, showing that the approximation method results in a reasonable optimality gap with respect to the exact MILP solution.