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Empirical Comparison of Uniformization Methods for Continuous-Time Markov Chains

  • John D. Diener
  • William H. Sanders

Abstract

Computation of transient state occupancy probabilities of continuous-time Markov chains is important for evaluating many performance, dependability, and performability models. A number of numerical methods have been developed to perform this computation, including ordinary differential equation solution methods and uniformization. The performance of these methods degrades when the highest departure rate in the chain increases with respect to a fixed time point. A new variant of uniformization, called adaptive uniformization (AU), has been proposed that can potentially avoid such degradation, when several state transitions must occur before a state with a high departure rate is reached. However, in general, AU has a higher time complexity than standard uniformization, and it is not clear, without an implementation, when All will be advantageous. This paper presents the results of three different AU implementations, differing in the method by which the “jump probabilities” are calculated. To evaluate the methods, relative to standard uniformization, a C++ class was developed to compute a bound on the round-off error incurred by each implementation, as well as count the number of arithmetic instructions that must be performed, categorized both by operation type and phase of the algorithm they belong to. An extended machine-repairman reliability model is solved to illustrate use of the class and compare the adaptive uniformization implementations with standard uniformization. Results show that for certain models and mission times, adaptive uniformization can realize significant efficiency gains, relative to standard uniformization, while maintaining the stability of standard uniformization.

Keywords

Layered Uniformization Uniformization Method Discrete Time Markov Chain Birth Process Standard Uniformization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1995

Authors and Affiliations

  • John D. Diener
    • 1
  • William H. Sanders
    • 2
  1. 1.Department of Electrical and Computer EngineeringThe University of ArizonaUSA
  2. 2.The Center for Reliable and High-Performance Computing Coordinated Science LaboratoryUniversity of Illinois at Urbana-ChampaignUSA

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