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Dataset used for INFOCOM 2019
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README.md

DeepTMA: Predicting Effective Contention Models for Network Calculus using Graph Neural Networks

This repository contains the dataset used for the paper "DeepTMA: Predicting Effective Contention Models for Network Calculus using Graph Neural Networks" publish at the 38th IEEE International Conference on Computer Communications (INFOCOM 2019). We refer to the paper for a full explanation of the methodology used for generating the dataset.

Reading the dataset

Each file is encoded using Protocol Buffers. The data structure is defined in dataset_infocom2019.proto and can be compiled to various target languages (e.g. Java, Python, Objective-C, and C++) using the protoc command line utility.

Example code in python

The script src/parse_example.py contains an example of how to parse the protobuf files using python. We first compile the .proto file to python:

$ sudo apt install python3-protobuf
$ git clone https://github.com/fabgeyer/dataset-infocom2019
$ cd dataset-infocom2019
$ protoc --python_out=src dataset.proto
$ python src/parse_example.py dataset/dataset.part0.pb.gz

Citation

If you use this dataset for your research, please include the following reference in any resulting publication:

@inproceedings{GeyerBondorf_INFOCOM2019,
	author    = {Geyer, Fabien and Bondorf, Steffen},
	title     = {{DeepTMA}: Predicting Effective Contention Models for Network Calculus using Graph Neural Networks},
	booktitle = {Proceedings of the 38th IEEE International Conference on Computer Communications (INFOCOM)},
	year      = {2019},
	month     = apr,
	address   = {Paris, France},
}
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