Virtual Network Embedding Algorithms Based on Best-Fit Subgraph Detection

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.

Memetic Elitist Pareto Evolutionary Algorithm for Virtual Network Embedding

“…The set : < = contains all virtual links and each virtual link is weighted by required bandwidth. Figure 1( (Shahin, 2015a;Shahin, 2015b;Cheng et al, 2011;Zhang et al, 2013), the revenue of embedding > ? at time A is the sum of all accommodated virtual resources of the virtual network request > ?…”

“…Some algorithms have been proposed to find optimal VNE solutions to be used as optimal bound for the heuristic based VNE solutions (Cheng et al, 2011;Botero et al, 2012). Shahin (2015b) proposed two VNE algorithms to find optimal VNE solutions. The proposed algorithms increase the acceptability of fragmented substrate networks by embedding virtual networks on best-fit sub-substrate networks.…”

“…Virtual Network Embedding Cost: as in (Shahin, 2015a;Shahin, 2015b), the cost of embedding > ? at time A is defined as total allocated substrate resources to > ?…”

“…To evaluate the proposed MEPDE-VNE algorithm, we compared its performance with the following algorithms: RW-MaxMatch (Cheng et al, 2011), RW-BFS (Cheng et al, 2011), BFSN-HEM (Shahin, 2015b), vnmFlib (Lischka & Karl, 2009), HCM (Shahin, 2015a), and BFSN (Shahin, 2015b).…”

“…Substrate network (SN): as in our previous work (Shahin, 2015a;Shahin, 2015b), SN is modeled as a graph 7 8 = 9 8 , : 8 , which is a weighted undirected graph. 9 8 is the set of all substrate nodes and : 8 is the set of all substrate links.…”