Quantitative system performance: computer system analysis using queueing network models
Quantitative system performance: computer system analysis using queueing network models
Robustness of queuing network formulas
Journal of the ACM (JACM)
First-order perturbation analysis of a simple multi-class finite source queue
Performance Evaluation
ACM Computing Surveys (CSUR)
The Operational Analysis of Queueing Network Models
ACM Computing Surveys (CSUR)
Approximate Methods for Analyzing Queueing Network Models of Computing Systems
ACM Computing Surveys (CSUR)
A spectral method for confidence interval generation and run length control in simulations
Communications of the ACM - Special issue on simulation modeling and statistical computing
Computer Performance Modeling Handbook
Computer Performance Modeling Handbook
Principles of Discrete Event Simulation
Principles of Discrete Event Simulation
Computation: finite and infinite machines
Computation: finite and infinite machines
Theory, Volume 1, Queueing Systems
Theory, Volume 1, Queueing Systems
Optimization of stochastic systems via simulation
WSC '89 Proceedings of the 21st conference on Winter simulation
Structural conditions for perturbation analysis of queueing systems
Journal of the ACM (JACM)
An overview of derivative estimation
WSC '91 Proceedings of the 23rd conference on Winter simulation
An axiomatic basis for general discrete-event modeling
WSC '91 Proceedings of the 23rd conference on Winter simulation
WSC '88 Proceedings of the 20th conference on Winter simulation
Performance continuity and differentiability in Monte Carlo optimization
WSC '88 Proceedings of the 20th conference on Winter simulation
Smoothed perturbation analysis algorithm for a G/G/1 routing problem
WSC '88 Proceedings of the 20th conference on Winter simulation
Single run optimization of a SIMAN model for closed loop flexible assembly systems
WSC '87 Proceedings of the 19th conference on Winter simulation
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Proving temporal properties of hybrid systems
WSC' 90 Proceedings of the 22nd conference on Winter simulation
Stochastic optimization using simulation: graphical representation of IPA estimation
Proceedings of the 33nd conference on Winter simulation
Proceedings of the 33nd conference on Winter simulation
Smoothed Perturbation Analysis for Stationary Single-ServerQueues with Multiple Customer Classes
Discrete Event Dynamic Systems
Proceedings of the 38th conference on Winter simulation
Computers and Operations Research
Variance properties of sample path derivatives of parametric random variables
Operations Research Letters
Infinitesimal perturbation analysis of a birth and death process
Operations Research Letters
Techniques for simulation response optimization
Operations Research Letters
An infinitesimal perturbation analysis algorithm for a multiclass G/G/1 queue
Operations Research Letters
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A rigorous extension of the recent perturbation analysis approach to more general discrete event systems is given. First, a general class of systems and performance measures is defined, and some basic reprsentational and linearity properties are derived. Next a sample gradient of performance with respect to a parameter of the system is defined. Then, for certain parameters of such systems, an infinitesimal perturbation analysis algorithm is derived. It is proved that this algorithm gives exact values for the sample gradients of performance with respect to the parameters, by observing only one sample path of the DEDS. (However, the sample gradient may or may not be a good estimate of the gradient of the performance measure; this point is elaborated in the body of the paper.) The computational complexity of this algorithm is bound to be linear in the number of events. These results offer the potential for very efficient calculation of the gradients—a fact that can be used for design/operation of computer systems, communication networks, manufacturing systems, and many other real-world systems, particularly since restrictive assumptions (e.g., exponential distributions) are not required of the system.