Model-Based Performance Predictions for SDN-Based Networks: A Case Study

  • Stefan HerrnlebenEmail author
  • Piotr Rygielski
  • Johannes Grohmann
  • Simon Eismann
  • Tobias Hoßfeld
  • Samuel Kounev
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12040)


Emerging paradigms for network virtualization like Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) form new challenges for accurate performance modeling and analysis tools. Therefore, performance modeling and prediction approaches that support SDN or NFV technologies help system operators to analyze the performance of a data center and its corresponding network. The Descartes Network Infrastructures (DNI) offers a high-level descriptive language to model SDN-based networks, which can be transformed into various predictive modeling formalisms. However, these modeling concepts have not yet been evaluated in a realistic scenario.

In this paper, we present an extensive case study evaluating the DNI modeling capabilities, the transformations to predictive models, and the performance prediction using the OMNeT++ and SimQPN simulation frameworks. We present five realistic scenarios of a content distribution network (CDN), compare the performance predictions with real-world measurements, and discuss modeling gaps and calibration issues causing mispredictions in some scenarios.


Network modeling Performance prediction Software-Defined Networking 



This work was funded by the German Research Foundation (DFG) under grant No. (KO 3445/18-1).


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Stefan Herrnleben
    • 1
    Email author
  • Piotr Rygielski
    • 2
  • Johannes Grohmann
    • 1
  • Simon Eismann
    • 1
  • Tobias Hoßfeld
    • 1
  • Samuel Kounev
    • 1
  1. 1.University of WürzburgWürzburgGermany
  2. 2.D4L data4life gGmbH, PotsdamPotsdamGermany

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