Virtual Network Embedding Algorithms Based on Best-Fit Subgraph Detection

TL;DR

This paper introduces two novel virtual network embedding algorithms that coarsen virtual networks with Heavy Edge Matching and embed them on best-fit subgraphs, significantly improving acceptance ratios and revenue in cloud datacenter networks.

Contribution

The paper presents new algorithms combining network coarsening and best-fit embedding to enhance virtual network acceptance and revenue in fragmented physical networks.

Findings

Increased acceptance ratio of virtual network requests.

Higher revenue compared to existing algorithms.

Effective handling of network fragmentation.

Abstract

One of the main objectives of cloud computing providers is increasing the revenue of their cloud datacenters by accommodating virtual network requests as many as possible. However, arrival and departure of virtual network requests fragment physical network's resources and reduce the possibility of accepting more virtual network requests. To increase the number of virtual network requests accommodated by fragmented physical networks, we propose two virtual network embedding algorithms, which coarsen virtual networks using Heavy Edge Matching (HEM) technique and embed coarsened virtual networks on best-fit sub-substrate networks. The performance of the proposed algorithms are evaluated and compared with existing algorithms using extensive simulations, which show that the proposed algorithms increase the acceptance ratio and the revenue.