Robustness of queuing network formulas
Journal of the ACM (JACM)
Guest Editor's Overview… Queuing Network Models of Computer System Performance
ACM Computing Surveys (CSUR)
The Operational Analysis of Queueing Network Models
ACM Computing Surveys (CSUR)
Performance bound hierarchies for queueing networks
ACM Transactions on Computer Systems (TOCS)
Balanced job bound analysis of queueing networks
Communications of the ACM
Computational algorithms for closed queueing networks with exponential servers
Communications of the ACM
Operational State Sequence Analysis
Performance '83 Proceedings of the 9th International Symposium on Computer Performance Modelling, Measurement and Evaluation
The impact of certain parameter estimation errors in queueing network models
PERFORMANCE '80 Proceedings of the 1980 international symposium on Computer performance modelling, measurement and evaluation
MANUPLAN: a precursor to simulation for complex manufacturing systems
WSC '85 Proceedings of the 17th conference on Winter simulation
SIGMETRICS '86/PERFORMANCE '86 Proceedings of the 1986 ACM SIGMETRICS joint international conference on Computer performance modelling, measurement and evaluation
Operational requirements for scalable search systems
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
| Hi-index | 0.00 |
Analytic models based on closed queuing networks (CQNS) are widely used for performance prediction in practical systems. In using such models, there is always a prediction error, that is, a difference between the predicted performance and the actual outcome. This prediction error is due both to modeling errors and estimation errors, the latter being the difference between the estimated values of the CQN parameters and the actual outcomes. This paper considers the second class of errors; in particular, it studies the effect of small estimation errors and provides bounds on prediction errors based on bounds on estimation errors. Estimation errors may be divided into two types: (1) the difference between the estimated value and the average value of the outcome, and (2) the deviation of the actual value from its average. The analysis first studies the sum of both types of errors, then the second type alone. The results are illustrated with three examples.