Flow-based Throughput Prediction using Deep Learning and Real-World Network Traffic
| Autor: | Christoph Hardegen, Sven Reißmann, Benedikt Pfülb, Alexander Gepperth, Sebastian Rieger |
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| Rok vydání: | 2019 |
| Předmět: |
Data stream
Artificial neural network Computer science business.industry Deep learning Pipeline (computing) 020206 networking & telecommunications Throughput 02 engineering and technology computer.software_genre Network topology Traffic flow (computer networking) Traffic engineering 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining Artificial intelligence business computer |
| Zdroj: | CNSM |
| Popis: | We present a processing pipeline for flow-based throughput classification based on a machine learning component using deep neural networks (DNNs) that is trained to predict the likely bit rate of a real-world network traffic flow ahead of time. The DNN is trained and evaluated on a flow data stream as well as on a reference dataset collected from a university data center. Predicted bit rates are quantized into three classes instead of the common binary classification into “mice” and “elephant” flows. An in-depth description of the data acquisition process, including preprocessing steps and anonymization used to protect sensitive information, is given. We employ t-SNE (a state-of-the-art data visualization algorithm) to visualize network traffic data, thus enabling us to analyze and understand the characteristics of network traffic data and relations between communication flows at a glance. Additionally, an architecture for flow-based routing utilizing the developed pipeline is proposed as a possible use-case. |
| Databáze: | OpenAIRE |
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