A guide to simulation (2nd ed.)
A guide to simulation (2nd ed.)
On the self-similar nature of Ethernet traffic
SIGCOMM '93 Conference proceedings on Communications architectures, protocols and applications
Analysis, modeling and generation of self-similar VBR video traffic
SIGCOMM '94 Proceedings of the conference on Communications architectures, protocols and applications
Experimental queueing analysis with long-range dependent packet traffic
IEEE/ACM Transactions on Networking (TON)
IEEE/ACM Transactions on Networking (TON)
On estimating the intensity of long-range dependence in finite and infinite variance time series
A practical guide to heavy tails
Experimental performance evaluation of batch means procedures for simulation output analysis
Proceedings of the 32nd conference on Winter simulation
A new heavy-tailed discrete distribution for LRD M/G/∞ sample generation
Performance Evaluation
ASAP3: a batch means procedure for steady-state simulation analysis
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Performance evaluation of a wavelet-based spectral method for steady-state simulation analysis
WSC '04 Proceedings of the 36th conference on Winter simulation
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Mean value estimation of processes exhibiting Long Range Dependence (LRD) requires a different approach than the techniques applied to those exhibiting Short Range Dependence (SRD), except for the independent replication method. We describe a nonoverlapping batch means method able to deal with LRD processes, the LRD Batch Means method. This method exploits the behavior of Asymptotically Second-order Self-Similar processes: their aggregated processes become well approximated by Fractional Gaussian Noise (FGN) processes for large aggregation levels. Once tested positively this similarity, the method produces a correlation-adjusted confidence interval from an empirical approximation of the distribution of the standardized average for the particular case of FGN processes. Afterwards, we measure its performance over both LRD and SRD processes.