@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}
}