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Artifact for "R-MPLS: Recursive Protection for Highly Dependable MPLS Networks"

Authors: Schmid, Stefan; Schou, Morten Konggaard; Srba, Jiri; Vanerio, Juan;

Artifact for "R-MPLS: Recursive Protection for Highly Dependable MPLS Networks"

Abstract

Artifact for the paper "R-MPLS: Recursive Protection for Highly Dependable MPLS Networks" accepted at CoNEXT 2022. The artifact contains the python source code of R-MPLS implemented on top of the MPLS data plane generator and simulator MPLS-Kit (accepted for Global Internet 2022), along with a topology dataset derived from topology-zoo in an adapted JSON format. The artifact comes with scripts to reproduce the experiments described in the paper and a Jupyter notebook to process the result files. The scripts are written in Bash and automate the execution of the MPLS-Kit python code. Additionally, we include a dataset containing the result files we obtained from executing the artifact’s scripts on our compute cluster. Finally, we provide instructions for executing the scripts and reproducing the results. (Only tested on / available for Linux)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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