Optimizing software defined network with energy effective structure based on novel method

The software-defined networking (SDN) is a networking technology that employs the application programming interfaces (APIs) for communicating and managing the architecture of the hardware behind a connection to the internet. An energy-effective SDN enhances the utilization of network resources by dynamically adjusting the usage of power in response to demands. It reduces the operating expenses for networking structure enhances the effectiveness and provides a minimal negative environmental impact. The major drawbacks of the study include initial set-up costs, security concerns, and dependency on centralized administration that influences the responsiveness and flexibility of the systems. In this research, we proposed a novel technique known as multidimensional krill herd-based effective linear regression model (MKH-ELRM) for providing an energy effectiveness structure with SDN. Initially, we gathered the SDN dataset for this study. In addition, we employed a robust standardization for preprocessing. We use a linear discriminant analysis (LDA) method for feature extraction. Furthermore, in the feature extraction process, we split the dataset into testing and training sets. As a result, we utilize some metrics for existing and proposed methods to examine the efficiency of SDN energy. Recall (93%), precision (94%), f1-score (95%), and accuracy (92%) are the outcomes of our proposed MKH-ELRM technique. The result attains a superior result, which provides an accurate energy efficiency with SDN when compared to other existing methods. In conclusion, MKH-ELRM outcomes compare to other current approaches and demonstrate the capacity for providing sustainable and operationally effective SDN networks in reducing energy usage. The implemented framework ensures scalability, adaptability, and long-term sustainability, offering significant potential for future smart network applications with improved energy efficiency and environmental friendliness.