Many designs for integrated services networks offer a bounded delay packet delivery service to support real-time applications. To provide a bounded delay service, networks must use admission control to regulate their load. Previous work on admission control mainly focused on algorithms that compute the worst case theoretical queueing delay to guarantee an absolute delay bound for all packets. In this paper, we describe a measurement-based admission control algorithm (ACA) for predictive service, which allows occasional delay violations. We have tested our algorithm through simulations on a wide variety of network topologies and driven with various source models, including some that exhibit long-range dependence, both in themselves and in their aggregation. Our simulation results suggest that measurement-based approach combined with the relaxed service commitment of predictive service enables us to achieve a high level of network utilization while still reliably meeting the delay bound.
Abstract The process forms heterocyclic MIDA boronate intermediates which are directly coupled with aryl halides to give aryl‐substituted heterocycles.
A detailed analysis is given of the geometric structure of a chaotic attractor observed from an extremely simple autonomous electrical circuit. It is third order, reciprocal, and has only one nonlinear element: a 3-segment piecewise-linear resistor. Extensive laboratory measurements from this circuit and a detailed geometrical analysis and computer simulation reveal the following rather intricate "anatomy" of the associated strange attractor. In addition to a microscopically infinite sheet-like composition the attractor has a macroscopic "double-scroll" structure, i.e., two sheetlike objects are curled up together into spiral forms with infinitely many rotations. (See frontispiece.) The chaotic nature of this circuit is further confirmed by calculating its associated Lyapunov exponents and Lyapunov dimension. The double-scroll attractor has one positive, one zero and one negative Lyapunov exponent. The Lyapunov dimension turns out to be a fractal between 2 and 3 which agrees with the observed structures. The power spectra of the three associated state variables obtained by both measurement and computer simulation show a continuous broad spectrum typical of chaotic systems.