Traffic Modeling Project¶
This is a traffic modeling engine suite of software tools. The input is a captured packet trace from a given point in the network. Models are application agnostic in that they are inferred from the connection sessions in the pcap file.
The project has been supported in part by the National Science Foundation award no. 1908974 CISE-CNS grant award.
modeling engine is developed in
python and uses
scikit-learn machine learning libraries.
Extracts a dataset from a raw packet trace file that are used by the traffic modeling engine
Generates traffic models using the datasets (or directly from a packet trace file).
Simulates the generation of traffic protocol data units (PDUs) on anetwork using a traffic model.
Provides analysis output for multiple network trace files, comparing them based on themetrics calculated from each input trace.
Traffic Generator Survey¶
Network traffic workloads are widely utilized in applied research to verify correctness and to measure the impact of novel algorithms, protocols, and network functions. We conducted a comprehensive survey of traffic generators referenced by researchers over the last 13 years, providing in-depth classification of the functional behaviors of the most frequently cited generators.
The survey paper is published at the ACM Computing Surveys.
We developed a tool to analyze about 7000 papers to identify popular traffic generators utilized in research.