Data Center Networks (DCNs) form the backbone of many Internet applications and services that have become essential in daily life. Energy consumption in these networks causes both economic and environmental issues. It is reported that 10% of global energy consumption is due to ICT and network usage. Computer networking equipment is designed to accommodate network traffic; however, the level of use of the equipment is not necessarily proportional to the power consumed by it. For example, DCNs do not always run at full capacity, yet the fact that they are supporting a lighter load is not reflected by a reduction in energy consumption. DCNs have been shown to unnecessarily over-consume energy when they are not fully loaded.

In this dissertation, we propose a new framework that reduces power consumption in software-defined DCNs. The proposed approach consists of a new Integer Programming model and a heuristic link utility-based algorithm that strives for a balance between energy consumption and performance. We evaluate the proposed framework using an experimental platform, which consists of a POX controller and the Mininet network emulator. Compared with the state-of-the-art approach, the Equal Cost Multi-Path (ECMP) algorithm, the results show that the proposed method reduces power consumption by up to 10% when the network is experiencing a high traffic load and 63.3% when the traffic load is low.

Based on these results, we outline how machine learning approaches could be used to further improve our approach to maintain the Quality of Service (QoS) at an acceptable level, specifically for real-time traffic using a Neural Network algorithm. The findings suggest that the proposed machine learning approach shows promising potential for optimizing power consumption while keeping the quality of the connection within SD-DCNs.