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
Experimental queueing analysis with long-range dependent packet traffic
IEEE/ACM Transactions on Networking (TON)
On the relevance of long-range dependence in network traffic
Conference proceedings on Applications, technologies, architectures, and protocols for computer communications
IEEE/ACM Transactions on Networking (TON)
A new heavy-tailed discrete distribution for LRD M/G/∞ sample generation
Performance Evaluation
Tail probabilities for a multiplexer with self-similar traffic
INFOCOM'96 Proceedings of the Fifteenth annual joint conference of the IEEE computer and communications societies conference on The conference on computer communications - Volume 3
A refined version of M/G/∞ processes for modelling VBR video traffic
Computer Communications
Modeling video traffic using M/G/∞ input processes: a compromise between Markovian and LRD models
IEEE Journal on Selected Areas in Communications
The M/G/∞ system revisited: finiteness, summability, long range dependence, and reverse engineering
Queueing Systems: Theory and Applications
A highly efficient M/G/∞ generator of self-similar traces
Proceedings of the 38th conference on Winter simulation
On the discrete-time g/gi/∞ queue*
Probability in the Engineering and Informational Sciences
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Several traffic measurement reports have convincingly shown the presence of self-similarity in modern networks, inducing as a result a revolution in the stochastic modeling of traffic. The use of self-similar processes in performance analysis has opened new problems and research issues in simulation studies, where the efficient generation of synthetic sample paths with self-similar properties is one of the fundamental concerns. In this paper, we present an M/G/∞ generator of self-similar traces, based on a highly efficient simulation model using the decomposition property of Poisson processes.