An n-state generalized probabilistic automaton/array maps a list of d states stochastically into a next state, resulting in a degree-d polynomially nonlinear transformation of the n-component state-probability vector, in contrast with the linear transformation given by the conventional Markovian model. This nonlinearity introduces the possibility of chaotic behaviour as time (iteration) progresses. It is shown that for two-state systems, the d = 2 case is nonchaotic, while chaotic behaviour is found for degree d as low as 5. Examples, and open issues for other values of n and/or d, are also noted.

An analytical performance modeling approach is presented for load sharing policies that schedule jobs based on a global system state. The main contributions of this work are: the derivation of analytical bounds to compute the average delay in large asymmetric systems under dynamic load sharing control, when job routing is based on the global system state; the experimental derivation of parameters which affect the implementation of load sharing policies at the user level; the demonstration that load sharing is effective even in the presence of high overheads; and the demonstration that in the asymmetric environment under study, carefully tuned algorithms for load sharing provide a significant improvement in performance over simpler algorithms.>

The design is discussed of the Locus family of networks rendering PC interface (PCI) service. Given are the expected user workload, the hardware costs, and the performance constraints. The workload consists of three classes of users: interactive, highly-interactive, and communication-intensive. The number of PCs is given and is equal to the number of users. It is shown that the problem can be reduced to a discrete capacity and flow assignment (CFA) problem. The backbone capacity assignment is inspired by the Lagrangian decomposition approach. It starts from a backbone capacity assignment which matches the initial flow and chooses a backbone server upgrade that gives the greatest response-time reduction per dollar. At this point, the flow deviation method is applied and iteration is continuous until the constraints are met. Several computational examples of network configuration synthesis are presented that emphasize the significance and generality of the results obtained.>

This paper is a survey and summary of some of the simpler one-input and two-input detection schemes. Particular emphasis is placed on those systems where less than a complete statistical description of the input is required. Such nonparametric detectors offer the advantages of ease of instrumentation and insensitivity to changes in the input statistics while yielding a fixed maximum false alarm rate. For both the one-input and two-input cases, the paper begins with a review of well-known parametric detectors, proceeds in the direction of increasing departure from the parametric noise model, and introduces corresponding nonparametric techniques.