Ego Network-based Virtual Network Embedding Scheme for Revenue Maximization

Abstract

Network Virtualization (NV) technology allows multiple virtual network requests to share resources on the same subtract network. In network virtualization, Virtual Network Embedding (VNE) is one of the main techniques used to map a virtual network to the substrate network. The effectiveness and efficiency of the virtual network are determined by the performance of the embedding algorithm. Hence, an efficient embedding algorithm is required to reduce the rejection rate and embed the maximum number of virtual networks which best fit the subtract network. In this article, we propose Ego Network-based Virtual Network Embedding (EN-ViNE) algorithm which aims to improve the performance of the embedding to accept more VNRs and increase the long-term revenue. We utilize the ego-network technique to search the nearest subtract nodes for embedding virtual nodes and found the shortest path between them for link embedding. The proposed scheme attempts to minimize the rejection of virtual network requests (VNRs) that are intended to maximize the long-term revenue for the substrate network provider. Extensive computer simulation reveals that the proposed scheme considerably outperforms the existing algorithms, topology-aware, and baseline for the long-term average revenue, acceptance ratio, and revenue/cost ratio.