DeViNE: A Decentralized Virtual Network Embedding Algorithm

TL;DR

DeViNE introduces a decentralized virtual network embedding algorithm that enhances scalability and robustness, outperforming centralized methods in acceptance rate and revenue-to-cost ratio.

Contribution

This paper presents a novel decentralized VNE algorithm that addresses scalability and robustness issues of prior centralized approaches using leader election and local embedding.

Findings

12% higher acceptance rate compared to existing methods.

Approximately 21% improvement in revenue-to-cost ratio.

Demonstrates enhanced scalability and robustness through decentralization.

Abstract

Virtual Network Embedding (VNE) is a technique for mapping virtual networks onto a physical network infrastructure, enabling multiple virtual networks to coexist on a shared physical network. Previous works focused on implementing centralized VNE algorithms, which suffer from lack of scalability and robustness. This project aims to implement a decentralized virtual network embedding algorithm that addresses the challenges of network virtualization, such as scalability, single point of failure, and DoS attacks. The proposed approach involves selecting L leaders from the physical nodes and embedding a virtual network request (VNR) in the local network of each leader using a simple algorithm like BFS. The algorithm then uses a leader-election mechanism for determining the node with the lowest cost and highest revenue and propagates the embedding to other leaders. By utilizing decentralization, we improve the scalability and robustness of the solution. Additionally, we evaluate the effectiveness of our fully decentralized algorithm by comparing it with existing approaches. Our algorithm performs $12\%$ better in terms of acceptance rate and improves the revenue-to-cost ratio by roughly $21\%$ to compared approaches.