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We propose a novel pricing incentive mechanism based on multi-satges traffic classification methodology supporting load balancing and allocating network resource efficiently for QoS-enabled networks in this paper. We integrate the pricing with QoS routing and present a novel pricing incentive mechanism meeting the QoS requirements of different applications. This mechanism provides an equitable pricing incentive for applications according to their service requests. A novel multi-stages traffic classification methodology that brings together the benefits of port mapping, signature matching and flow character classification techniques is motivated by variety of network activities and their QoS requirements of traffic. We study the pricing and different levels of services in detail and integrate admission control scheme and load balancing in our framework. By theoretical analysis and extensive simulations, we prove its effectiveness in making traffic load balance and providing an incentive for users to utilize network resources to users’ satisfaction

Accurate network traffic identification plays important roles in many areas such as traffic engineering, QoS and intrusion detection etc. The emergence of many new encrypted applications which use dynamic port numbers and masquerading techniques causes the most challenging problem in network traffic identification field. One of the challenging issues for existing traffic identification methods is that they can’t classify online encrypted traffic. To overcome the drawback of the previous identification scheme and to meet the requirements of the encrypted network activities, our work mainly focuses on how to build an online Internet traffic identification based on flow information. We propose realtime encrypted traffic identification based on flow statistical characteristics using machine learning in this paper. We evaluate the effectiveness of our proposed method through the experiments on different real traffic traces. By experiment results and analysis, this method can classify online encrypted network traffic with high accuracy and robustness

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