Effective bandwidths at multi-class queues
Queueing Systems: Theory and Applications
Effective bandwidths for the multi-type UAS channel
Queueing Systems: Theory and Applications
On the asymptotic behavior of heterogeneous statistical multiplexer with applications
IEEE INFOCOM '92 Proceedings of the eleventh annual joint conference of the IEEE computer and communications societies on One world through communications (Vol. 2)
Finite buffer discrete-time queues with multiple Markovian arrivals and services in ATM networks
IEEE INFOCOM '92 Proceedings of the eleventh annual joint conference of the IEEE computer and communications societies on One world through communications (Vol. 3)
IEEE/ACM Transactions on Networking (TON)
On the self-similar nature of Ethernet traffic
SIGCOMM '93 Conference proceedings on Communications architectures, protocols and applications
Effective bandwidths for multiclass Markov fluids and other ATM sources
IEEE/ACM Transactions on Networking (TON)
Methods for performance evaluation of VBR video traffic models
IEEE/ACM Transactions on Networking (TON)
Switching and Traffic Theory for Integrated Broadband Networks
Switching and Traffic Theory for Integrated Broadband Networks
INFOCOM '95 Proceedings of the Fourteenth Annual Joint Conference of the IEEE Computer and Communication Societies (Vol. 2)-Volume - Volume 2
IEEE Journal on Selected Areas in Communications
Hi-index | 0.00 |
The main contributions of this paper are two-fold. First, we prove fundamental, similarly behaving lower and upper bounds, and give an approximation based on the bounds, which is effective for analyzing ATM multiplexers, even when the traffic has many, possibly heterogeneous, sources and their models are of high dimension. Second, we apply our analytic approximation to statistical models of video teleconference traffic, obtain the multiplexing system's capacity as determined by the number of admissible sources for given cell loss probability, buffer size and trunk bandwidth, and, finally, compare with results from simulations, which are driven by actual data from coders. The results are surprisingly close. Our bounds are based on Large Deviations theory. Our approximation has two easily calculated parameters, one is from Chernoff's theorem and the other is the system's dominant eigenvalue. A broad range of systems are analyzed and the time for analysis in each case is a fraction of a second.