Simulating Stable Stochastic Systems, I: General Multiserver Queues
Journal of the ACM (JACM)
Simulating Stable Stochastic Systems, II: Markov Chains
Journal of the ACM (JACM)
Analysis of system bottlenecks using a queueing network model
Proceedings of the SIGOPS workshop on System performance evaluation
A Statistical Study of the Performance of a Task Scheduling Algorithm
IEEE Transactions on Computers
Queueing Network Models for Parallel Processing with Asynchronous Tasks
IEEE Transactions on Computers
Bandwidth of Crossbar and Multiple-Bus Connections for Multiprocessors
IEEE Transactions on Computers
Variance estimation and sequential stopping in steady-state simulations using linear regression
ACM Transactions on Modeling and Computer Simulation (TOMACS)
| Hi-index | 0.01 |
We consider the estimation via simulation of confidence intervals for steady-state response variables for stochastic systems which have a regenerative stochastic structure. Sequential stopping rules are investigated which allow the ratio of the width to the midpoint of an estimated confidence interval to be specified ahead of time. We prove that the resulting confidence intervals are valid asymptotically as the relative width decreases to zero. For various relative widths we empirically investigate the validity of the confidence intervals obtained when the stopping rules are applied to the simulation of queuing systems having a regenerative stochastic structure. For the queuing systems and response variables considered, a relative width of 0.05 is found to be sufficiently small to yield valid confidence intervals in almost all cases. In addition, we empirically compare the sequential stopping rules with a fixed stopping rule.