Using Heavy Clique Base Coarsening to Enhance Virtual Network Embedding

TL;DR

This paper introduces a novel virtual network embedding algorithm that uses heavy clique coarsening and a refined Kernighan-Lin algorithm to improve acceptance ratios and revenue in network virtualization.

Contribution

It presents a new embedding method combining heavy clique coarsening with Kernighan-Lin refinement to enhance virtual network acceptance and revenue.

Findings

Increased acceptance ratio compared to existing algorithms

Higher revenue achieved through improved embedding efficiency

Effective coarsening technique enhances overall performance

Abstract

Network virtualization allows cloud infrastructure providers to accommodate multiple virtual networks on a single physical network. However, mapping multiple virtual network resources to physical network components, called virtual network embedding (VNE), is known to be non-deterministic polynomial-time hard (NP-hard). Effective virtual network embedding increases the revenue by increasing the number of accepted virtual networks. In this paper, we propose virtual network embedding algorithm, which improves virtual network embedding by coarsening virtual networks. Heavy Clique matching technique is used to coarsen virtual networks. Then, the coarsened virtual networks are enhanced by using a refined Kernighan-Lin algorithm. The performance of the proposed algorithm is evaluated and compared with existing algorithms using extensive simulations, which show that the proposed algorithm improves virtual network embedding by increasing the acceptance ratio and the revenue.