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
ACM SIGCOMM Computer Communication Review
Self-Similar Network Traffic and Performance Evaluation
Self-Similar Network Traffic and Performance Evaluation
Proceedings of the 33nd conference on Winter simulation
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
IEEE Internet Computing
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
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
Do you trust your software-based traffic generator?
IEEE Communications Magazine
The bittorrent p2p file-sharing system: measurements and analysis
IPTPS'05 Proceedings of the 4th international conference on Peer-to-Peer Systems
Computer Networks: The International Journal of Computer and Telecommunications Networking
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In the process of network traffic modeling, for simulation purposes, there is often a need for statistical description of traffic data sources. Usually, the network traffic is measured by capturing packets at a physical level. Normally, the estimation of statistical description of traffic data sources cannot be derived directly from such captured packets traffic. For that reason, we have researched for simpler solutions, which are based on the estimation of statistical processes of traffic data sources from the measured packet network traffic. We have developed the estimation methods, which allow the estimation of suitable probability distribution functions and their parameters of stochastic processes of traffic data sources. Statistical distributions of network traffic processes, such as data lengths process and data inter-arrival time, are important since they can be used for modeling of network traffic in simulation tools. For that reason, the estimation method is firstly developed, which mimics the defragmentation process. This method allows an estimation of distributions of data source network traffic processes and their parameters for captured packet traffic.During further testing, this method shows some limitations, especially for the process of data lengths. For that reason, we have developed a new estimation method with the approach described in this paper in further detail. In the new estimation method, which is called estimation method based on histogram comparison (EMHC), we use the opposite concept where distribution of data lengths is transformed by a developed analytical model to a packet size's histogram. The latter is further compared to a packet size histogram of captured packet traffic. The optimization method is used to find such distribution parameters of the data length process that cause minimal discrepancies between the histogram of captured packets and the estimated packet size histogram. To estimate the discrepancy between two histograms, a well-known 脧聡2 test is used, which is modified by a weighting function that considers, beside packet frequencies, the packet lengths as well. The proposed algorithm and method are confirmed through validations and experiments in a simulation tool.