Abstract
In previous decades, along with the continuous evolution of the Internet Protocol-based network, numerous Internet programs that have played critical roles in the current technological era are being developed inclusive of interactive applications like VoIP, video conferencing with audio, and video streaming. Nevertheless, the transmission performance and user experience of these instant services that require high bandwidth or low latency may suffer when serious traffic congestion occurs. In approaching this issue, two mechanisms – Integrated Service and Differentiated Service – were proposed in traditional IP networks to provide quality of service (QoS) with the existing network architecture; however, results have been unsuccessful due to limitations in hardware devices and complexities from the scalability. Software-defined networking in recent years has emerged, where the decoupling of the control plane and data plane altering the original operations makes it possible to provide flexible networking monitoring, routing, and management. In this paper, we implement a QoS management system with a dynamic routing algorithm based on the software-defined networking architecture. It aims to generate an optimal route with minimum cost based on real-time monitored statistics and the cost model. Moreover, not only is it integrated with a flow classification engine based on machine learning algorithms, but the system is also equipped with a bandwidth management mechanism that can enforce rate-limit policies for each application with different priority under traffic congestion. Experimental results show that the proposed algorithm can provide excellent QoS with little overhead for both file transferring and streaming services. In addition, several test cases were designed to verify the bandwidth management mechanism, where the result shows that QoS policies can be fulfilled in one to several seconds for different applications.