On the Validity of Long-Run Estimation Methods for Discrete-Event Systems Peter J. Haas" Peter W. Glynnt Performance evaluation of computer systems, networks, and applications often involves analysis of long-run system characteristics. Many characteristics of interest can be expressed as time-average limits of the form r(f) = ,-~oo ? / t f(x(~)) du, lira 1 where f is a real-valued function and { X ( t ) : t _> 0 } is the underlying stochastic process that records the state of the system as it evolves over continuous time. In this paper we assume that { X(t) : t > 0 } can be represented as a generalized semi-Markov process (GSMP) and consider simulationbased methods for obtaining point estimates and confidence intervals for time-average limits. We also consider timeaverage limits of the form n--1 j=O where { (S~, Cn) : n _> 0 } is the general state space Markov chain used to define the GSMP (see below). When the output process { f(X(t)): t > 0 } or { ](Sn, Cn): n > 0} obeys a central limit theorem (CLT), there are two main approaches to obtaining an asymptotic confidence interval for the time-average limit. The first
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On the validity of long-run estimation methods for discrete-event systems
Haas, Peter J.; Glynn, Peter W.
Follow ACM SIGMETRICS Performance Evaluation Review
, Volume 30 (3)
Association for Computing Machinery – Dec 1, 2002
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