An efficient approximate technique for solving fluid models

Abstract

Stochastic fluid-flow models have been widely used as an important tool for the analysis of a variety of computer and communication models. In particular, when the event rates of the system under investigation vary in orders of magnitude, the use of fluid models results in considerable computational savings when compared to traditional models where all events are explicitly represented. This is true for instance, in the so called <i>performability</i> models [10], where events that represent structural changes in the system (e.g., failure and repair events) occur at much lower rates than those associated with some performance measure, such as the arrival and service of jobs. As another example, consider a queueing model of a communication network channel. The intervals between events associated with packet arrival and departure from a buffer may be orders of magnitude smaller than the intervals that represent changes in the arrival rate.