Towards Efficient and Scalable Machine Learning-Based QoS Traffic Classification in Software-Defined Network

Abstract

Internet Service Provider (ISP) has the responsibility to fulfill the Quality of Service (QoS) of various types of applications. The centralized network controller in Software Defined Networking (SDN) provides the chance to instil intelligence in managing network resources based on QoS requirements. A fined-grained QoS Traffic Engineering can be realized by identifying different traffic flow types and categorizing them according to various application/classes. Previous methods include port-based classification and Deep Packet Inspection (DPI), which have been found non-accurate and highly computational. Thus, machine learning (ML) based traffic classifier has gained much attention from the research community, which can be seen from an increase number of works being published. This paper identifies the issues in ML-based traffic classification (TC) in order to devised the best solution; i.e. the TC framework should be scalable to accommodate network expansion, can accurately identify flows according to their source applications/classes, while maintaining an efficient run-time and memory requirement. Therefore, based on these findings, this work proposed a TC engine comprises of Training and Feature Selection Module and Classifier Model, which is placed at the data plane. The training and feature selection will be done offline and regularly to keep the Classifier Model updated. In the proposed solution, the SDN switch forwards the packets the Classifier Model, which classify the packets with accurate applications and send them to the control plane. Finally, the controller will perform resource and queue management according to the labeled packets and updates the flow tables via the switch. The proposed solution will be the starting point in solving efficiency and scalability issues in SDN-ISP TC.