Memetic Elitist Pareto Evolutionary Algorithm for Virtual Network Embedding

TL;DR

This paper introduces MEPE-VNE, a memetic elitist Pareto evolutionary algorithm that improves virtual network embedding by optimizing acceptance ratio, revenue, and cost through multi-objective evolutionary strategies and local search.

Contribution

It presents a novel memetic elitist Pareto evolutionary algorithm, MEPE-VNE, combining NSGA-II and local search for enhanced virtual network embedding performance.

Findings

Increased acceptance ratio and revenue.

Reduced substrate network cost.

Faster convergence compared to existing algorithms.

Abstract

Assigning virtual network resources to physical network components, called Virtual Network Embedding, is a major challenge in cloud computing platforms. In this paper, we propose a memetic elitist pareto evolutionary algorithm for virtual network embedding problem, which is called MEPE-VNE. MEPE-VNE applies a non-dominated sorting-based multi-objective evolutionary algorithm, called NSGA-II, to reduce computational complexity of constructing a hierarchy of non-dominated Pareto fronts and assign a rank value to each virtual network embedding solution based on its dominance level and crowding distance value. Local search is applied to enhance virtual network embedding solutions and speed up convergence of the proposed algorithm. To reduce loss of good solutions, MEPE-VNE ensures elitism by passing virtual network embedding solutions with best fitness values to next generation. Performance of the proposed algorithm is evaluated and compared with existing algorithms using extensive simulations, which show that the proposed algorithm improves virtual network embedding by increasing acceptance ratio and revenue while decreasing the cost incurred by substrate network.