PEPERCORN: Inferring Performance Models from Location Tracking Data

  • Nikolas Anastasiou
  • William Knottenbelt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8054)


Stochastic performance models are widely used to analyse the performance of systems that process customers and resources. However, the construction of such models is traditionally manual and therefore expensive, intrusive and prone to human error. In this paper we introduce PEPERCORN, a Petri Net Performance Model (PNPM) construction tool, which, given a dataset of raw location tracking traces obtained from a customer-processing system, automatically formulates and parameterises a corresponding Coloured Generalised Stochastic Petri Net (CGSPN) performance model.


Performance Modelling Location Tracking Data Mining Coloured Generalised Stochastic Petri Nets 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Anastasiou, N., Horng, T.-C., Knottenbelt, W.: Deriving Generalised Stochastic Petri Net performance models from High-Precision Location Tracking Data. In: Proc. 5th Intl. Conference on Performance Evaluation Methodologies and Tools, VALUETOOLS 2011 (2011)Google Scholar
  2. 2.
    Anastasiou, N., Knottenbelt, W.: Deriving Coloured Generalised Stochastic Petri Net Performance Models from High-Precision Location Tracking Data. In: Proc. 4th ACM/SPEC International Conference on Performance Engineering (2013)Google Scholar
  3. 3.
    Anastasiou, N., Knottenbelt, W., Marin, A.: Automatic Synchronisation Detection in Petri Net Performance Models Derived from Location Tracking Data. In: Thomas, N. (ed.) EPEW 2011. LNCS, vol. 6977, pp. 29–41. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Dingle, N.J., Knottenbelt, W.J., Suto, T.: PIPE2: A tool for the Performance Evaluation of Generalised Stochastic Petri Nets. ACM SIGMETRICS Performance Evaluation Review 36(4), 34–39 (2009)CrossRefGoogle Scholar
  5. 5.
    Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proc. 2nd Intl. Conf. on Knowledge Discovery and Data Mining, KDD 1996 (1996)Google Scholar
  6. 6.
    Horng, T.-C., Anastasiou, N., Knottenbelt, W.: LocTrackJINQS: An Extensible Location-aware Simulation Tool for Multiclass Queueing Networks. In: Proc. 5th Intl. Workshop on Practical Applications of Stochastic Modelling (2011)Google Scholar
  7. 7.
    Thümmler, A., Buchholz, P., Telek, M.: A Novel Approach for Phase-Type Fitting with the EM Algorithm. IEEE Transactions on Dependable and Secure Computing 3, 245–258 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Nikolas Anastasiou
    • 1
  • William Knottenbelt
    • 1
  1. 1.Department of ComputingImperial College LondonLondonUK

Personalised recommendations