Ant colony optimization based energy efficient virtual network embedding

Abstract

The rapid proliferation of data centers has significantly increased energy consumption and green house gas emissions. Attention has focused on greening the data centers. Energy efficient virtual network embedding (EE-VNE) has been studied to save energy consumption in data centers, which has been proved to be NP-hard. Especially, when considering multiple data centers with evolving virtual network resources requirements, it becomes much more challenging to approach an optimal solution in a reasonable amount of time. We propose an Ant Colony Optimization based Energy Efficient Virtual Network Embedding and scheduling (ACO-EE-VNE) to minimize energy usage in multiple data centers for both computing and network resources by modeling the EE-VNE as a construction graph. In addition, we reduce the space complexity of ACO-EE-VNE by developing a novel way to track and update the pheromone. Our extensive evaluation results show that our ACO-EE-VNE could reduce energy consumption up to 52% and double the acceptance ratio compared with existing virtual network embedding algorithms.