Automated Extraction of Network Traffic Models Suitable for Performance Simulation.

Data centers are increasingly becoming larger and dynamic due to virtualization. In order to leverage the performance modeling and prediction techniques, such as Palladio Component Model or Descartes Modeling Language, in such a dynamic environments, it is necessary to automate the model extraction. Building and maintaining such models manually is not feasible anymore due to their size and the level of details. This paper is focused on traffic models that are an essential part of network infrastructure. Our goal is to decompose real traffic dumps into models suitable for performance prediction using Descartes Network Infrastructure modeling approach. The main challenge was to efficiently encode an arbitrary signal in the form of simple traffic generators while maintaining the shape of the original signal. We show that a typical 15 minute long tcpdump trace can be compressed to 0.4-15% of its original size whereas the relative median of extraction error is close to 0% for the most of the 69 examined traces.