A Decision Tree-Based Online Traffic Classification Method for QoS Routing in Data Center Networks

Abstract

In data center networks, how to ensure the quality of service (QoS) of traffic effectively has become an important optimization goal. Traffic classification has been widely studied to guarantee network flow QoS since it provides a basis for the network performance level requirement of different flow types. However, traditional network traffic classification methods, which can only identify traffic type offline, have no ability for online classification and the in-time QoS guarantee. To solve this problem, we propose an online traffic classification for QoS routing (OCQR) framework based on programmable data plane in this paper. Firstly, in order to improve the accuracy of flow classification and reduce the impact on line-rate packet forwarding, the way of machine learning model offline training and online deployment is used to classify the network flow in real time. Based on the hardware foundation of programmable switch and the complexity of machine learning algorithm, a CART decision tree model is used to classify network flows. Secondly, in order to actually deploy and run the decision tree model in the programmable switch with limited computing and storage resources, we prune the decision tree model and optimize the leaf nodes to reduce the size of the decision tree and improve the classification recall rate. Finally, by building a P4 simulation environment, the recall rate of OCQR’s classified scheduling of some application flows reaches more than 98%, which is consistent with the evaluation results of the offline dataset. At the same time, compared with the classification method based on SDN, OCQR does not require additional bandwidth occupation, so it has less impact on network performance.

A Study on Prediction of Size and Morphology of Ag Nanoparticles Using Machine Learning Models for Biomedical Applications

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