Novel adaptive virtual network embedding algorithm for Cloud’s private backbone network

In this paper, we study the adaptive virtual network embedding problem within Cloud’s backbone. The main idea is to take profit from the unused bandwidth but allocated to virtual networks. Consequently, the acceptance rate of new clients will be maximized. However, the congestion rate of virtual links must be minimized in order to maximize the satisfaction of end-users. To do so, first we formulate the problem as K-supplier optimization problem. Then, we propose a novel virtual network embedding strategy denoted by Adaptive-VNE. It is based on the approximation-algorithm for bottleneck problems and backtracking strategy. The proposal is validated by simulations and experimental testbed. The results obtained show that Adaptive-VNE outperforms the most prominent strategies and reaches a good performance.