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Titel:

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

Dokumenttyp:
Forschungsdaten
Veröffentlichungsdatum:
19.02.2021
Verantwortlich:
Geyer, Fabien
Autorinnen / Autoren:
Geyer, Fabien; Scheffler, Alexander; Bondorf, Steffen
Institutionszugehörigkeit:
TUM
Herausgeber:
TUM
Identifikator:
doi:10.14459/2020mp1596901
Enddatum der Datenerzeugung:
26.10.2020
Fachgebiet:
DAT Datenverarbeitung, Informatik
Quellen der Daten:
Simulationen / simulations
Datentyp:
Datenbanken / data bases
Methode der Datenerhebung:
The datasets contain a set of networks evaluated using network calculus. Each network contains a series of servers and flows, and the result of their analysis. The datasets are encoded using pbzlib. The full description of the dataset creation method and the evaluation are explained in the paper.
Beschreibung:
Dataset used for the paper “Tightening Network Calculus Delay Bounds by Predicting Flow Prolongations in the FIFO Analysis” presented at IEEE RTAS 2021. Abstract of the publication: Network calculus offers the means to compute worst-case traversal times based on interpreting a system as a queueing network. A major strength of network calculus is its strict separation of modeling and analysis frameworks. That is, a model is purely descriptive and can be put into multiple different analyses t...     »
Schlagworte:
Dataset; Deep Learning; Network Calculus
Technische Hinweise:
View and download (407 MB, 5 files)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1596901):
rsync rsync://m1596901@dataserv.ub.tum.de/m1596901/
Sprache:
en
Rechte:
by-sa, http://creativecommons.org/licenses/by-sa/4.0
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