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

arXiv: https://arxiv.org/abs/1504.06855 · HTML: https://mlsanity.com/paper/1504.06855/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2015.

## Built on by (most cited first, 2 of 2)

- [Monkey Business: Reinforcement learning meets neighborhood search for Virtual Network Embedding](https://mlsanity.com/paper/2202.13706/) (2022)
- [A multi-domain virtual network embedding algorithm with delay prediction](https://mlsanity.com/paper/2202.01473/) (2022)

## Abstract (arXiv)

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.