COSTPN for Modeling and control of telecommunication systems

  • Hermann de Meer
  • Oliver-Rainer Düsterhöft
  • Stefan Fischer
Performance Analysis with Stochastic Petri Nets
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1605)


The design of modern telecommunication systems is a complex task since many parameters, mostly of stochastic nature, have to be taken into account in order to achieve desired performance values. Stochastic Petri Nets (SPNs) are a well-known modeling and analysis tool for such systems. In addition, the ability to adapt system operations to quickly changing environment or system conditions is of great importance. Therefore, a new framework for the extension SPNs is presented in this paper which introduces elements providing means for a dynamic optimization of performability measures. A new type of transition is defined offering a feature for specification of controlled switching, called reconfiguration, from one set of markings of a SPN to another set of markings. In a numerical analysis, these optional reconfiguration transitions are evaluated in order to optimize a specified reward or cost function. The result of the analysis is a set of strategies which tell the controller of the system when to fire enabled reconfiguration transitions and when to remain in the current state. The extended SPNs are called COSTPNs (COn-trolled STochastic Petri Nets). For the numerical analysis, COSTPNs are mapped on EMRMs (Extended Markov Reward Models). Computational analysis is possible with algorithms adopted from Markov decision theory, including transient and stationary optimization. This paper introduces the new COSTPN model, discusses the algorithms necessary for the mapping of COSTPNs on EMRMs and shows how COSTPNs can be applied for the modeling and control of a typical telecommunications system, namely a multimedia server. Major emphasis is put on the introduction of new enabling and firing rules for reconfiguring transitions and on the illustration of the new modeling approach by means of the multimedia server example.


stochastic Petri nets performability dynamic optimization extended Markov reward models Markov decision theory 


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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Hermann de Meer
    • 1
  • Oliver-Rainer Düsterhöft
    • 1
  • Stefan Fischer
    • 2
  1. 1.Department of Computer ScienceUniversity of HamburgHamburg
  2. 2.Praktische Informatik IVUniversity of MannheimMannheim

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