Memetic Multi-Objective Particle Swarm Optimization-Based Energy-Aware Virtual Network Embedding

TL;DR

This paper introduces an energy-aware virtual network embedding algorithm using multi-objective particle swarm optimization, aiming to optimize resource utilization and reduce energy consumption in cloud data centers.

Contribution

It proposes a novel multi-objective PSO-based algorithm with local search for energy-efficient virtual network embedding, improving solution quality and convergence speed.

Findings

Enhanced revenue in virtual network embedding

Reduced energy consumption in physical networks

Faster convergence of the optimization algorithm

Abstract

In cloud infrastructure, accommodating multiple virtual networks on a single physical network reduces power consumed by physical resources and minimizes cost of operating cloud data centers. However, mapping multiple virtual network resources to physical network components, called virtual network embedding (VNE), is known to be NP-hard. With considering energy efficiency, the problem becomes more complicated. In this paper, we model energy-aware virtual network embedding, devise metrics for evaluating performance of energy aware virtual network-embedding algorithms, and propose an energy aware virtual network-embedding algorithm based on multi-objective particle swarm optimization augmented with local search to speed up convergence of the proposed algorithm and improve solutions quality. 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 revenue and decreasing energy consumption.