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Dataset used for IFIP Networking 2019
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dataset First commit May 9, 2019
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README.md

README.md

DeepMPLS: Fast Analysis of MPLS Configurations Using Deep Learning

This repository contains the dataset used for the paper "DeepMPLS: Fast Analysis of MPLS Configurations Using Deep Learning" publish at the IFIP Networking 2019 conference. We refer to the paper for a full explanation of the methodology used for generating the dataset.

Reading the dataset

This dataset is based on the networks from the topology-zoo dataset. The repository contains two types of files:

  • Network description files, stored as a tar.gz archive, containing the topologies and the MPLS rules in XML format. The XML format can be processed using P-Rex.
  • Queries files in compressed JSON format.

The matching between network descriptions and queries files is done via the filenames (see example below).

The dataset/qpred folder corresponds to the Satisfiability and Routing tasks in the paper, and the dataset/cpred folder corresponds to the Partial synthesis task.

Example query in the JSON files

In dataset/cpred/topology-zoo/Arpanet196912.queries.json.gz, the first query is:

{
  "query": "<11> SRI .* UCLA <> 0",
  "query_result": true,
  "network": "s1p49"
}

This query corresponds to the network described by the s1p49/topo.xml and s1p49/routing.xml files from the archive dataset/cpred/topology-zoo/Arpanet196912.xmls.tgz.

Citation

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

@inproceedings{GeyerSchmid_Networking2019,
	author    = {Geyer, Fabien and Schmid, Stefan},
	title     = {{DeepMPLS}: Fast Analysis of MPLS Configurations Using Deep Learning},
	booktitle = {Proceedings of the 18th IFIP Networking Conference},
	year      = {2019},
	month     = mai,
	address   = {Warsaw, Poland},
}
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