A Path Generation Approach to Embedding of Virtual Networks

TL;DR

This paper introduces a scalable, one-shot virtual network embedding method using column generation, achieving near-optimal accuracy and improved resource utilization compared to existing approaches.

Contribution

It presents a novel path-based, one-shot VNE solution with column generation that balances embedding accuracy and scalability, outperforming current methods.

Findings

Achieves about 95% of the optimal acceptance ratio.

Improves VN acceptance ratio and resource utilization.

Reduces time complexity by 92% for large networks.

Abstract

As the virtualization of networks continues to attract attention from both industry and academia, the Virtual Network Embedding (VNE) problem remains a focus of researchers. This paper proposes a one-shot, unsplittable flow VNE solution based on column generation. We start by formulating the problem as a path-based mathematical program called the primal, for which we derive the corresponding dual problem. We then propose an initial solution which is used, first, by the dual problem and then by the primal problem to obtain a final solution. Unlike most approaches, our focus is not only on embedding accuracy but also on the scalability of the solution. In particular, the one-shot nature of our formulation ensures embedding accuracy, while the use of column generation is aimed at enhancing the computation time to make the approach more scalable. In order to assess the performance of the proposed solution, we compare it against four state of the art approaches as well as the optimal link-based formulation of the one-shot embedding problem. Experiments on a large mix of Virtual Network (VN) requests show that our solution is near optimal (achieving about 95% of the acceptance ratio of the optimal solution), with a clear improvement over existing approaches in terms of VN acceptance ratio and average Substrate Network (SN) resource utilization, and a considerable improvement (92% for a SN of 50 nodes) in time complexity compared to the optimal solution.