Large-scale Networked Systems Research Articles

Discrete-Event Simulation Of Network Reliability And Markovian Models

This paper presents an enhanced reliability modelling and evaluation methodology using discrete-event simulation techniques. The proposed simulation methodology has the capability of estimating reliability, mean time to failure, and probability density function of time to failure for a complex network system. Two types of reliability simulation models are presented: a descriptive network simulation model and a direct Markov simulation model. Both models are capable of solving complex systems consisting of components with general failure rates. The discrete-event reliability models require only descriptive knowledge of network configuration rather than analytical network characteristics, such as cuts and ties. Examples are given to demonstrate the accuracy, computational ease, and reliability analysis capability of these models. SIMAN is the simulation language used for both reliability modelling and evaluation. The reliability modelling technique applied through discrete-event simulation is modular, thus increasing its adaptability to any large-scale complex network system

A high-speed digital neural network chip with low-power chain-reaction architecture

A high-speed digital neural network chip adopts a polyhedric discrimination neuron (PDN) model and low-power chain-reaction (LCR) architecture that can reduce the power dissipation to one-fiftieth or less. The chip contains 832 fully implemented digital synapse units that form 13 neurons on a 10.3-mm*14.1-mm die using 0.8- mu m CMOS technology. The synapse weights are updated using an external computer. A computational speed of 8 billion connections per second (GCPS) is achieved with low 54-mW power dissipation. The forward propagation time is 104 ns. These features make it possible to implement large-scale neural network chips and systems.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Simplified Optimization of Water Supply SystemS

A procedure is presented for optimization of large-scale water supply network systems. Pipeline costs and energy costs are considered in the optimization. The optimization problem is solved through an iterative procedure in which the diameter of each pipe sequentially is adjusted according to the ratio between marginal pipeline and energy costs. The procedure is applicable to branched as well as to looping networks. No rounding of diameters are required since available dimensions are used directly in the optimization. The procedure assumes that all nodal inflow and outflow requirements are known and that minimal service pressure requirements are specified. The procedure has been implemented and has been successfully applied to economic optimization of several large-scale water supply problems.

エントロピーを評価とするネットワークシステム理論

With the development of science and technology, large scale systems are produced. The number of elements in a system grows large and the relations between the elements also grow complex. When we intend to construct and control these systems, it is impossible to treat them by the ways which are on the viewpoint of microscopic observation. Therefore it is necessary to introduce a new expression and a general performance index of the system on the viewpoint of macroscopic observation.In this paper, the system is limited to be a network system such as a traffic system. We introduce the idea of entropy into the stationary state of that system and consider the significance of the evaluation of network system by entropy.As a result, it is proved that the negative entropy introduced has the same character as the entropy increasing law of statistical mechanics and that the negative entropy also expresses the degree of confusion of the network system. Therefore by making use of the negative entropy as the performance index, we obtain the construction theorem which is intuitively understood. We believe the usefulness of entropy as the performance index of a network system. Last of all we obtain a useful decomposition theorem for the construction of a large scale network system.