User: Guest  Login
Title:

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

Document type:
Forschungsdaten
Publication date:
19.02.2021
Responsible:
Geyer, Fabien
Authors:
Geyer, Fabien; Scheffler, Alexander; Bondorf, Steffen
Author affiliation:
TUM
Publisher:
TUM
Identifier:
doi:10.14459/2020mp1596901
End date of data production:
26.10.2020
Subject area:
DAT Datenverarbeitung, Informatik
Resource type:
Simulationen / simulations
Data type:
Datenbanken / data bases
Description:
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...     »
Method of data assessment:
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.
Key words:
Dataset; Deep Learning; Network Calculus
Technical remarks:
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/
Language:
en
Rights:
by-sa, http://creativecommons.org/licenses/by-sa/4.0
 BibTeX