Abstract
Numerical techniques for modeling computer networks under nonstationary conditions are discussed, and two distinct approaches are presented. The first approach uses a queuing theory formulation to develop differential equation models which describe the behavior of the network by time-varying probability distributions. In the second approach, a nonlinear differential equation model is developed for representing the dynamics of the network in terms of time-varying mean quantities. This approach allows multiple classes of traffic to be modeled and establishes a framework for the use of optimal control techniques in the design of network control strategies. Numerical techniques for determining the queue behavior as a function of time for both approaches are discussed and their computational advantages are contrasted with simulation. >
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