JoWUA

Abstract

Software-Defined Networking (SDN) enhances network management by separating the control and data planes into controllers and switches, allowing for centralized, programmable networks with multiple controllers. Switches are mapped to controllers and exchange control messages to manage the network, which leads to significant energy consumption. Managing energy in networks has become a critical issue, as dynamic changes in switch loads can cause controller overloads, necessitating the migration of switches to other controllers. As networks grow, energy consumed in control communications becomes a major concern. This paper proposes an unsupervised learning Artificial Neural Network (ANN) model to address controller overloads and optimize energy consumption, achieving faster execution times compared to conventional methods while maintaining manageable energy efficiency. The model considers dynamic switch loads and the hop distance between switches and controllers when remapping switches to optimize energy use. Experimental results demonstrate that the proposed unsupervised ANN model performs effectively in large networks, enabling efficient handling of controller overloads during variations in switch loads. The adaptability of the ANN model provides a robust strategy for energy-efficient load distribution, enhancing the scalability and efficiency of SDN environments.