Publications VS: Bibliography 2021 BibTeX
@inproceedings {INPROC-2021-01,
author = {David Hellmanns and Lucas Haug and Moritz Hildebrand and Frank D{\"u}rr and Stephan Kehrer and Ren{\'e} Hummen},
title = {{How to Optimize Joint Routing and Scheduling Models for TSN Using Integer Linear Programming}},
booktitle = {Proceedings of the 29th International Conference on Real-Time Networks and Systems},
editor = {ACM},
address = {Nantes},
publisher = {ACM (Online)},
institution = {University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Germany},
pages = {1--12},
type = {Conference Paper},
month = {April},
year = {2021},
isbn = {10.1145/3453417.3453421},
keywords = {Time-Sensitive Networking, TSN, Scheduling, Routing, Integer Linear Programming, Optimization, Model, ILP},
language = {English},
cr-category = {D.4.7 Operating Systems Organization and Design},
ee = {ftp://ftp.informatik.uni-stuttgart.de/pub/library/ncstrl.ustuttgart_fi/INPROC-2021-01/INPROC-2021-01.pdf},
contact = {david.hellmanns@ipvs.uni-stuttgart.de},
department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems},
abstract = {Reliable real-time communication is an essential technology for industrial
manufacturing but also other branches to transport mission-critical messages.
IEEE Time-Sensitive Networking (TSN) is a disruptive real-time communication
standard extending IEEE Ethernet with real-time mechanisms. One of the core
features of TSN is the Time-Aware Shaper (TAS) enabling TDMA-based scheduling
of streams within the network. TDMA has many advantages from the real-time
perspective. Foremost, stream isolation in the time dimension enables tight
delay and jitter bounds. Moreover, conformance to these bounds is proven by the
design of the TDMA schedule. However, calculating an optimal schedule is an
NP-hard problem. Therefore, various approaches to optimize the schedule
calculation are proposed, such as Integer Linear Programming (ILP).
Nevertheless, a systematic comparsion of the different optimization approaches
with respect to their performance is missing so far. To fill this gap, we first
provide a systematic classification of optimizations of ILP-based TSN
scheduling. To quantify the effects of such optimization approaches, we
introduce a base ILP and propose optimizations for the different categories.
Using the proposed optimization, we evaluate the performance with regard to
execution time and schedulability (number of solved schedules). Our results
show that the optimizations lead to strongly fluctuating results. Certain
intuitive optimizations can even lead to massive performance degradations.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2021-01&engl=1}
}
@article {ART-2021-02,
author = {Otto Bibartiu and Frank D{\"u}rr and Kurt Rothermel and Beate Ottenw{\"a}lder and Andreas Grau},
title = {{Scalable k-out-of-n models for dependability analysis with Bayesian networks}},
journal = {Reliability Engineering \& System Safety},
editor = {Paolo Gardoni},
publisher = {Elsevier Science Ltd.},
volume = {210},
pages = {1--13},
type = {Article in Journal},
month = {February},
year = {2021},
isbn = {10.1016/S0951-8320(21)00145-9},
keywords = {Availability; Scalability; Voting Gate; Fault-Tree; Bayesian networks},
language = {English},
cr-category = {B.8.1 Reliability, Testing, and Fault-Tolerance,
C.4 Performance of Systems},
ee = {ftp://ftp.informatik.uni-stuttgart.de/pub/library/ncstrl.ustuttgart_fi/ART-2021-02/ART-2021-02.pdf,
https://doi.org/10.1016/j.ress.2021.107533},
contact = {otto.bibartiu@ipvs.uni-stuttgart.de},
department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems},
abstract = {Availability analysis is indispensable in evaluating the dependability of
safety and business-critical systems, for which fault tree analysis (FTA) has
proven very useful throughout research and industry. Fault trees (FT) can be
analyzed by means of a rich set of mathematical models. One particular model
are Bayesian networks (BNs) which have gained considerable popularity recently
due to their powerful inference abilities. However, large-scale systems, as
found in modern data centers for cloud computing, pose modeling challenges that
require scalable availability models. An equivalent BN of a FT has no scalable
representation for the k-out-of-n (k/n) voting gate because the conditional
probability table that constitutes the k/n voting gate grows exponentially in
n. Thus, the memory becomes the limiting factor. We propose a scalable k/n
voting gate representation for BNs, based on the temporal noisy adder. The
resulting model reduces the initial exponential to polynomial memory growth
without a custom inference algorithm. Previous BN implementations of the k/n
voting gate could only handle around 30 input events until memory limits make
inference infeasible. However, our evaluation shows that our scalable model can
handle more than 700 input events per gate, making it possible to evaluate
large scale systems.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=ART-2021-02&engl=1}
}
