2 research outputs found
A Novel Network Optimization Framework Based on Software-Defined Networking (SDN) and Deep Learning (DL) Approach
Full text linkAccess 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
Dynamic QoS: Automatically Modifying QoS Queue's Maximum Bandwidth Rate-Limit of Network Devices for Network Improvement
Full text linkThe heterogeneous data traffic of today's network is a huge challenge to existing best-effort network technology, particularly in the context of large Ethernet, which handles hundreds to thousands of users. The existing conventional best-effort network technology is no longer efficient to handle the diversity of traffic types in the network and requires network management equipment such as Quality of Service (QOS). Usually, QOS is implemented on the gateway router. However, for better network performance and management, to guarantee high priority for sensitive traffic like video conferencing, Voice over Internet Protocol (VoIP), and streaming media within an internal network, it is nice to have QoS implemented on each router in the LAN network, starting from the access router to the gateway router. This paper is to demonstrate the effectiveness of the proposed dynamic QoS that has been developed and deployed in the LAN, purposely to provide adequate bandwidth for sensitive traffic when the network utilization is high and congested, by automatically modifying the QoS Queue's Maximum Bandwidth Rate-Limit of the best-effort traffic queue of the related router. The performance of the proposed developed dynamic QoS was evaluated via a comparison study before and after the dynamic QoS was presented in the network simulation environment that was built using Mininet. Results from the testing show that the developed dynamic QoS can improve the network's performance by automatically giving the appropriate bandwidth for sensitive traffic on the fly while needed/on demand
