Although ATM seems to be the wave of the future, one analysis requires that the utilization of the network be quite low. That analysis is based on asymptotic decay rates of steady-state distributions used to develop a concept of effective bandwidths for connection admission control. The present authors have developed an exact numerical algorithm that shows that the effective-bandwidth approximation can overestimate the target small blocking probabilities by several orders of magnitude when there are many sources that are more bursty than Poisson. The bad news is that the appealing simple connection admission control algorithm using effective bandwidths based solely on tail-probability asymptotic decay rates may actually not be as effective as many have hoped. The good news is that the statistical multiplexing gain on ATM networks may actually be higher than some have feared. For one example, thought to be realistic, the analysis indicates that the network actually can support twice as many sources as predicted by the effective-bandwidth approximation. The authors also show that the effective bandwidth approximation is not always conservative. Specifically, for sources less bursty than Poisson, the asymptotic constant grows exponentially in the number of sources (when they are scaled as above) and the effective-bandwidth approximation can greatly underestimate the target blocking probabilities. Finally, they develop new approximations that work much better than the pure effective-bandwidth approximation.

Models for predicting the performance of multiplexed variable bit rate video sources are important for engineering a network. However, models of a single source are also important for parameter negotiations and call admittance algorithms. In this paper we propose to model a single video source as a Markov renewal process whose states represent different bit rates. We also propose two novel goodness-of-fit metrics which are directly related to the specific performance aspects that we want to predict from the model. The first is a leaky bucket contour plot which can be used to quantify the burstiness of any traffic type. The second measure applies only to video traffic and measures how well the model can predict the compressed video quality.>

The performance of a packet voice multiplexer queue in which the less significant bits of voiced packets are dropped during states of congestion in the multiplexer is examined. Using the results of simulation and analytical modeling, it is illustrated that bit dropping of voice packets significantly smooth the burstiness of superposition packet voice traffic by speeding up the packet service rate during critical periods of congestion in the queue. The smoothing effect renders it possible to approximate the superposition by a Poisson process for modeling a packet voice multiplexer with bit dropping. By comparison with a simulation, an analytical model based on the Poisson assumption is shown to produce quite accurate performance predictions. The results indicate that significant capacity and performance advantages are gained in the multiplexer as a result of the bit-dropping scheme.>

The authors analyze the performance of an end-to-end window-based flow control subject to bandwidth management. In particular, they model a high-level data-link-control-type layer-two protocol, e.g. LAPD, based on positive acknowledgements for correctly received frames. They explicitly model the effect of frame losses on the performance, which takes on a greater significance when some type of bandwidth management is used by the network. When a frame is lost, there are two major recovery mechanisms: REJECT and time-out. Both of them are explicitly incorporated into the analysis. The main performance measure considered is the goodput, i.e. the throughput of frames excluding retransmissions, that the customer can achieve. When the network is at a given level of congestion, it is shown that a customer can choose an appropriate window size to optimize the goodput subject to bandwidth management.>

The authors examine the performance of a packet voice multiplexer queue in which the less significant bits on voice packets are dropped during states of congestion in the multiplexer. Using the results of simulation and analytical modeling, it is illustrated that bit dropping on voice packets significantly smoothes the superposition packet voice traffic by speeding up the packet service rate during critical periods of congestion in the queue. This phenomenon renders it possible to approximate the superposition process by a Poisson process for analyzing a packet voice multiplexer with bit dropping. The multiplexer is modeled using an M/D/1/K model in which D denotes the deterministic but state-dependent nature of service. By comparison with a simulation, this model is shown to produce quite accurate performance predictions.>

We study the performance of a statistical multiplexer whose inputs consist of a superposition of packetized voice sources and data. The performance analysis predicts voice packet delay distributions, which usually have a stringent requirement, as well as data packet delay distributions. The superposition is approximated by a correlated Markov modulated Poisson process (MMPP), which is chosen such that several of its statistical characteristics identically match those of the superposition. Matrix analytic methods are then used to evaluate system performance measures. In particular, we obtain moments of voice and data delay distributions and queue length distributions. We also obtain Laplace-Stieitjes transforms of the voice and data packet delay distributions, which are numerically inverted to evaluate tails of delay distributions. It is shown how the matrix analytic methodology can incorporate practical system considerations such as finite buffers and a class of overload control mechanisms discussed in the literature. Comparisons with simulation show the methods to be accurate. The numerical results for the tails of the voice packet delay distribution show the dramatic effect of traffic variability and correlations on performance.