A Novel Network Optimization Framework Based on Software-Defined Networking (SDN) and Deep Learning (DL) Approach

Access to networks and the Internet has multiplied, and data traffic is growing exponentially and quickly. High network utilization, along with varied traffic types in the network, poses a considerable challenge and impact on the ICT Infrastructure, particularly affecting the performance and responsiveness of real-time application users who will experience slowness and poor performance. Conventional/traditional Quality of Service (QoS) mechanisms, designed to ensure reliable and efficient data transmission, are increasingly insufficient due to their static nature and inability to adapt to the dynamic demands of modern networks. As such, this study introduces a Novel Network Optimization Framework leveraging the combined strengths of Software-Defined Networking (SDN) and Deep Learning (DL) to dynamically manage multiple QoS of network devices in enterprise and campus network environments. The proposed system is a dynamic QoS that utilizes SDN's global monitoring and centralized management control capabilities to programmatically control network devices, ensuring that sensitive traffic is allocated with appropriate bandwidth and minimized latency. Concurrently, DL algorithms enhance the framework's decision-making process by proposing an accurate preferred configuration for the best adequate bandwidth for sensitive traffic transmission. This integration facilitates real-time adjustments to network conditions and improves overall network performance by ensuring high-priority applications receive the bandwidth they require without manual/human intervention. By providing a dynamic, intelligent solution to QoS management, this framework represents a significant step forward in developing more adaptable, resilient, and efficient networks capable of supporting the demands of contemporary and future digital ecosystems.