Introduction to queueing theory (2nd ed)
Introduction to queueing theory (2nd ed)
On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
Wide area traffic: the failure of Poisson modeling
IEEE/ACM Transactions on Networking (TON)
Self-similarity in World Wide Web traffic: evidence and possible causes
Proceedings of the 1996 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Long-lasting transient conditions in simulations with heavy-tailed workloads
Proceedings of the 29th conference on Winter simulation
Discrete-event simulation
Evidence for long-tailed distributions in the internet
IMW '01 Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement
Activity periods of an infinite server queue and performance of certain heavy tailed fluid queues
Queueing Systems: Theory and Applications
The Busy Period of the M/GI/∞ Queue
Queueing Systems: Theory and Applications
The Structural Cause of File Size Distributions
MASCOTS '01 Proceedings of the Ninth International Symposium in Modeling, Analysis and Simulation of Computer and Telecommunication Systems
Improving confidence in network simulations
Proceedings of the 38th conference on Winter simulation
Models and framework for supporting runtime decisions in Web-based systems
ACM Transactions on the Web (TWEB)
Fast simulation of self-similar and correlated processes
Mathematics and Computers in Simulation
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
| Hi-index | 0.00 |
For statistical inference based on telecommunications network simulation, we examine the effect of a heavy-tailed file-size distribution whose corresponding density follows an inverse power law with exponent α + 1, where the shape parameter α is strictly between 1 and 2. Representing the session-initiation and file-transmission processes as an infinite-server queueing system with Poisson arrivals, we derive the transient conditional mean and covariance function that describes the number of active sessions as well as the steady-state counterparts of these moments. Assuming the file size (service time) for each session follows the Lomax distribution, we show that the variance of the sample mean for the time-averaged number of active sessions tends to zero as the power of 1 − α of the simulation run length. Therefore, impractically large sample-path lengths are required to achieve point estimators with acceptable levels of statistical accuracy. This study compares the accuracy of point estimators based on the Lomax distribution with those for lognormal and Weibull file-size distributions whose parameters are determined by matching their means and a selected extreme quantile with those of the Lomax. Both alternatives require shorter run lengths than the Lomax to achieve a given level of accuracy. Although the lognormal requires longer sample paths than the Weibull, it better approximates the Lomax and leads to practicable run lengths in almost all scenarios.