Unleashing the Power of Paying Multiplexing Only Once in Stochastic Network Calculus

@article{Bouillard2022UnleashingTP,
  title={Unleashing the Power of Paying Multiplexing Only Once in Stochastic Network Calculus},
  author={Anne Bouillard and Paul Nikolaus and Jens B. Schmitt},
  journal={Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems},
  year={2022}
}
  • Anne Bouillard, Paul Nikolaus, J. Schmitt
  • Published 29 April 2021
  • Computer Science
  • Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems
The stochastic network calculus (SNC) holds promise as a versatile and uniform framework to calculate probabilistic performance bounds in networks of queues. A great challenge to accurate bounds and efficient calculations are stochastic dependencies between flows due to resource sharing inside the network. However, by carefully utilizing the basic SNC concepts in the network analysis the necessity of taking these dependencies into account can be minimized. To that end, we unleash the power of… 
Unleashing the Power of Paying Multiplexing Only Once in Stochastic Network Calculus
TLDR
The unleashed PMOO analysis is extended to the partially dependent case and a case study of a canonical example topology, known as the diamond network, is provided, displaying favourable results over the state of the art SNC calculations.

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