Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
Generating representative Web workloads for network and server performance evaluation
SIGMETRICS '98/PERFORMANCE '98 Proceedings of the 1998 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Characterizing user behavior and network performance in a public wireless LAN
SIGMETRICS '02 Proceedings of the 2002 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Simulation Modeling and Analysis
Simulation Modeling and Analysis
A high-level programming environment for packet trace anonymization and transformation
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
Continuous random variate generation by fast numerical inversion
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Realistic and responsive network traffic generation
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
HttpTools: a toolkit for simulation of web hosts in OMNeT++
Proceedings of the 2nd International Conference on Simulation Tools and Techniques
LiTGen, a lightweight traffic generator: application to P2P and mail wireless traffic
PAM'07 Proceedings of the 8th international conference on Passive and active network measurement
On the role of flows and sessions in internet traffic modeling: an explorative toy-model
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
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We present in this paper RENETO, a packet-level traffic generator for OMNeT++/INET. In order to achieve realistic traffic behavior, a first tool computes a model by doing an automatic analysis of a real traffic capture. This analysis extracts statistical distributions of different parameters of the model (e.g., packet size, inter-arrival time). Based on this first step, we then generate traffic in the OMNeT++ simulator corresponding to the observed behavior. Related traffic analysis and generator often model each studied parameter as one statistical distribution, thus treating them as statistically independent. With this work, we use the concept of linking some parameters and making them correlated, in order to mimic more accurately traffic patterns seen in reality. We apply our method to both UDP and TCP based traffic.