An autonomic traffic classification system for network operation and management

Abstract

Traffic classification is an important aspect in network operation and management, but challenging from a research perspective. During the last decade, several works have proposed different methods for traffic classification. Although most proposed methods achieve high accuracy, they present several practical limitations that hinder their actual deployment in production networks. For example, existing methods often require a costly training phase or expensive hardware, while their results have relatively low completeness. In this paper, we address these practical limitations by proposing an autonomic traffic classification system for large networks. Our system combines multiple classification techniques to leverage their advantages and minimize the limitations they present when used alone. Our system can operate with Sampled NetFlow data making it easier to deploy in production networks to assist network operation and management tasks. The main novelty of our system is that it can automatically retrain itself in order to sustain a high classification accuracy along time. We evaluate our solution using a 14-day trace from a large production network and show that our system can sustain an accuracy <96 %, even in presence of sampling, during long periods of time. The proposed system has been deployed in production in the Catalan Research and Education network and it is currently being used by network managers of more than 90 institutions connected to this network.