A QoS-aware framework for traffic classificationin software-defined networks
Tutor / director / evaluatorAkyldiz, Ian
Document typeMaster thesis (pre-Bologna period)
Rights accessRestricted access - author's decision
[ANGLÈS] How can we build a system for traffic classification so that different QoS levels can be identified? How can we analyse different network parameters and identify a specific QoS for every flow? The purp ose of this work is to build a system capable of classifying traffic according to flows' different QoS requirements. Next, classification results are going to b e used to improve the state-of-the-art traffic engineering techniques in SDN networks. As in SDN networks ele- ments are programmable from the controller, different QoS paths can b e implemented so that different traffic flows can see diferent QoS. This traffic classification system go es b eyond the classical state-of-the-art classification into mice/elephant flows. We want to identify different priority classes so that higher priority classes are able to access to more network resources. We seek improving the overall network p erformance. The approach for tackling this problem would be somehow equal to the approach used for building a new machine learning system. We are going to define a set of variables, measure them, and classify traffic according to the values of that variables into different classes. We will start defining a measurement layer for SDN. To classify trafic we first need to observe what prop erties is exhibiting a traffic flow. This first layer will enables us to measure different flow prop erties as well as p erforming other management tasks. We are going to define a layer so that we could gather flow information with minimal network over-head. This system will b e implemented distributely in the switches and will leverage flow tables switches to improve its functionalities. Second we are going to use the information captured to calculate various flow statistical fingerprints so that we can infer their network resources requirements. We will target only QoS signifficant flows. We are going to develop a machine learning algorithm in the scop e of semi-sup ervised learning that would learn form lab elled and unlab elled data to infer the most likely QoS. In particular, we are going to apply a machine learning algorithm known as Laplacian SVM to classify various network traffic flows into different QoS classes. Finally, we will prototyp e this algorithm and try it in a real world data set.[CASTELLÀ] En este trabajo se desarrolla un sistema para clasificar trafico basado en machine learning para redes definidas por software.[CATALÀ] En aquest treball es desenvolupa un sistema per classificar tràfic basat en machine learning per xarxes definides per software.