On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
Analysis, modeling and generation of self-similar VBR video traffic
SIGCOMM '94 Proceedings of the conference on Communications architectures, protocols and applications
Wide area traffic: the failure of Poisson modeling
IEEE/ACM Transactions on Networking (TON)
Long-lasting transient conditions in simulations with heavy-tailed workloads
Proceedings of the 29th conference on Winter simulation
Self-similarity in World Wide Web traffic: evidence and possible causes
IEEE/ACM Transactions on Networking (TON)
Dynamics of IP traffic: a study of the role of variability and the impact of control
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Self-Similar Network Traffic and Performance Evaluation
Self-Similar Network Traffic and Performance Evaluation
A compound model for TCP connection arrivals for LAN and WAN applications
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Advances in modeling and engineering of Longe-Range dependent traffic
Fractal Traffic Models for Internet Simulation
ISCC '00 Proceedings of the Fifth IEEE Symposium on Computers and Communications (ISCC 2000)
On the relationship between file sizes, transport protocols, and self-similar network traffic
ICNP '96 Proceedings of the 1996 International Conference on Network Protocols (ICNP '96)
Modeling IP traffic using the batch Markovian arrival process
Performance Evaluation - Modelling techniques and tools for computer performance evaluation
Modeling IP traffic: joint characterization of packet arrivals and packet sizes using BMAPs
Computer Networks: The International Journal of Computer and Telecommunications Networking
Proceedings of the 35th conference on Winter simulation: driving innovation
Self-Similar Processes in Telecommunications
Self-Similar Processes in Telecommunications
High-Speed Network Traffic Acquisition for Agent Systems
IAT '07 Proceedings of the 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Internet traffic modeling by means of Hidden Markov Models
Computer Networks: The International Journal of Computer and Telecommunications Networking
Analyzing the Network Traffic Requirements of Multiplayer Online Games
ADVCOMP '08 Proceedings of the 2008 The Second International Conference on Advanced Engineering Computing and Applications in Sciences
Mathematical model of IRIS replication mechanism for the simulation of tactical networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Markov models of internet traffic and a new hierarchical MMPP model
Computer Communications
The bittorrent p2p file-sharing system: measurements and analysis
IPTPS'05 Proceedings of the 4th international conference on Peer-to-Peer Systems
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Knowing the estimation of a statistical process's parameters for measured network traffic is very important as it can then be further used for the statistical analyses and modeling of network traffic in simulation tools. It is for this reason that different estimation methods are proposed that allow estimations of the statistical processes of network traffic. One of them is our own histograms comparison (EMHC) based method that can be used to estimate statistical data-length process parameters from measured packet traffic. The main part of EMHC method is Mapping Algorithm with Fragmentation Mimics (MAFM). The MAFM algorithm allows the estimation of a theoretical packet-size histogram for different distributions of the data-length process. In this paper describes in detail the limitations of a developed algorithm, which are correlates with the long-range dependence of data-length distribution. It is shown that a developed MAFM algorithm has limited usability for distribution types which do not posses the finite value of an expected value. In order to improve the robustness for such types of distribution, the new parameter ULS (Upper Limit of Summa) is involved in the original MAFM algorithm. The ULS parameter limits the tail of the distribution. By assuming a finite ULS value, the MAFM algorithm can now be used for all distributions of the data-length process, as well as for distributions without a defined expected value, such as Pareto. The presented analytical results have been confirmed by experiments through the use of the simulation tool.