Introduction to queueing theory (2nd ed)
Introduction to queueing theory (2nd ed)
Fitting Equations to Data: Computer Analysis of Multifactor Data
Fitting Equations to Data: Computer Analysis of Multifactor Data
Workload Characterization Issues and Methodologies
Performance Evaluation: Origins and Directions
Introduction to modeling and generating probabilistic input processes for simulation
Winter Simulation Conference
Regularized sparse representation for spectrometric pulse separation and counting rate estimation
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Estimation for nonhomogeneous Poisson processes from aggregated data
Operations Research Letters
Some results on successive failure times of a system with minimal instantaneous repairs
Operations Research Letters
Modeling clustered non-stationary Poisson processes for stochastic simulation inputs
Computers and Industrial Engineering
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Central problems in the performance evaluation of computer systems are the description of the behavior of the system and characterization of the workload. One approach to these problems comprises the interactive combination of data-analytic procedures with probability modeling. This paper describes methods, both old and new, for the statistical analysis of non-stationary univariate stochastic point processes and sequences of positive random variables. Such processes arefr equently encountered in computer systems. As an illustration of the methodology an analysis is given of the stochastic point process of transactions initiated in a running data base system. On theb asis of the statistical analysis, a non-homogeneous Poissonp rocess model for the transaction initiation process is postulated for periods of high system activity and found to be an adequate characterization of the data. For periods of lower system activity, the transaction initiation process has a complex structure, with more clustering evident. Overall models of this type have application to the validation of proposed data base subsystem models.