Tightening Network Calculus Delay Bounds by Predicting Flow Prolongations in the FIFO Analysis

This repository contains the dataset used for the paper "Tightening Network Calculus Delay Bounds by Predicting Flow Prolongations in the FIFO Analysis" publish at the 27th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS 2021). We refer to the paper for a full explanation of the methodology used for generating the dataset.

Citation

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

@inproceedings{GeyerSchefflerBondorf_RTAS2021,
	author    = {Geyer, Fabien and Scheffler, Alexander and Bondorf, Steffen},
	title     = {Tightening Network Calculus Delay Bounds by Predicting Flow Prolongations in the {FIFO} Analysis},
	booktitle = {Proceedings of the 27th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS 2021)},
	year      = {2021},
	month     = may,
	doi       = {10.1109/RTAS52030.2021.00021},
}

Getting the dataset

The raw dataset can be accessed via the DOI: 10.14459/2020mp1596901. The following command can be used to download the full dataset via FTP:

$ wget -r ftp://m1596901:[email protected]/

The dataset is comprised of three files:

• dataset-train.pbz is the dataset used for training the GNN
• dataset-evaluation.pbz is the dataset used for evaluation in Section VI, except for Subsection VI-C
• dataset-evaluation-large.pbz is the dataset used for evaluation in Section VI-C

Additionally, dataset_structure.proto details the datastructure used for the dataset.

Reading the dataset

The dataset is stored as serialized protobuf messages using the Python library pbzlib. Alternative programming languages may be used with pbzlib (e.g. Java, Go).

This repository contains an example python script for parsing the files. To get it and execute it:

$ git clone https://github.com/fabgeyer/dataset-rtas2021.git
$ cd dataset-rtas2021
$ pip3 install -r requirements.txt
$ python3 example.py path/to/dataset-train.pbz

License

The data in this repository is licensed under Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).