RUB » Faculty of Mathematics » Center of Computer Science » DNet | Distributed and Networked Systems 

Publications

2021

  • Steffen Bondorf, Binbin Chen, Jonathan Scarlett, Haifeng Yu and Yuda Zhao. Sublinear-Time Non-Adaptive Group Testing with O(k log n) Tests via Bit-Mixing Coding. In IEEE Transactions on Information Theory, vol. 67, no. 3, March 2021 (@IEEE, @ArXiv, bib)

Accepted Publications

  • Fabien Geyer, Alexander Scheffler and Steffen Bondorf. Tightening Network Calculus Delay Bounds by Predicting Flow Prolongations in the FIFO Analysis. Accepted to IEEE RTAS 2021. (bib)

IEEE Transactions Early Access

  • Fabien Geyer and Steffen Bondorf. Graph-based Deep Learning for Fast and Tight Network Calculus Analyses. In IEEE Transactions on Network Science and Engineering (@IEEE, bib)

2020

  • Steffen Bondorf and Fabien Geyer. Virtual Cross-Flow Detouring in the Deterministic Network Calculus Analysis. In Proc. of IFIP Networking (@IFIP, @IEEE, bib)
  • Bruno Cattelan, Steffen Bondorf and Alberto E. Schaeffer-Filho. An Empirical Study of Tightest Network Calculus Analyses for Networks with Multicast Flows. In Proc. of IEEE COMPSAC (@IEEE, bib)
  • Bruno Cattelan and Steffen Bondorf. On Delay Bounds and Measurements: A COTS Testbed for Network Performance Experimentation. In Proc. of IEEE ADMNET (IEEE COMPSAC Workshops) (@IEEE, bib)
  • Fabien Geyer and Steffen Bondorf. On the Robustness of Deep Learning-predicted Contention Models for Network Calculus. In Proc. of IEEE ISCC (@IEEE, @ArXiv, bib)

2019

  • Fabien Geyer and Steffen Bondorf. On the Robustness of Deep Learning-predicted Contention Models for Network Calculus. arXiv:1911.10522v1 [cs.NI], 24 Nov 2019. (@ArXiv, bib)