Reliability models for dataflow computer systems

The demands for concurrent operation within a computer system and the representation of parallelism in programming languages have yielded a new form of program representation known as data flow (DENN 74, DENN 75, TREL 82a). A new model based on data flow principles for parallel computations and parallel computer systems is presented. Necessary conditions for liveness and deadlock freeness in data flow graphs are derived. The data flow graph is used as a model to represent asynchronous concurrent computer architectures including data flow computers

Construction and Verification of Performance and Reliability Models

Over the last two decades formal methods have been extended towards performance and reliability evaluation. This paper tries to provide a rather intuitive explanation of the basic concepts and features in this area. Instead of striving for mathematical rigour, the intention is to give an illustrative introduction to the basics of stochastic models, to stochastic modelling using process algebra, and to model checking as a technique to analyse stochastic models

Life and reliability models for helicopter transmissions

Computer models of life and reliability are presented for planetary gear trains with a fixed ring gear, input applied to the sun gear, and output taken from the planet arm. For this transmission the input and output shafts are co-axial and the input and output torques are assumed to be coaxial with these shafts. Thrust and side loading are neglected. The reliability model is based on the Weibull distributions of the individual reliabilities of the in transmission components. The system model is also a Weibull distribution. The load versus life model for the system is a power relationship as the models for the individual components. The load-life exponent and basic dynamic capacity are developed as functions of the components capacities. The models are used to compare three and four planet, 150 kW (200 hp), 5:1 reduction transmissions with 1500 rpm input speed to illustrate their use

Software Reliability Models

The problem considered here is the building of Non-homogeneous Poisson Process (NHPP) model. Currently existing popular NHPP process models like Goel-Okumoto (G-O) and Yamada et al models suffer from the drawback that the probability density function of the inter-failure times is an improper density function. This is because the event no failure in (0, oo] is allowed in these models. In real life situations we cannot draw sample(s) from such a population and also none of the moments of inter-failure times exist. Therefore, these models are unsuitable for modelling real software error data. On the other hand if the density function of the inter-failure times is made proper by multiplying with a constant, then we cannot assume finite number of expected faults in the system which is the basic assumption in building the software reliability models. Taking these factors into consideration, we have introduced an extra parameter, say c, in both the G -0 and Yamada et al models in order to get a new model. We find that a specific value of this new parameter gives rise to a proper density for inter-failure times. The G -0 and Yamada et al models are special cases of these models corresponding to c = 0. This raises the question - “Can we do better than existing G -0 and Yamada et al models when 0 \u3c c \u3c 1 ?”. The answer is ‘yes’. With this objective, the behavior of the software failure counting process { N ( t ) , t \u3e 0} has been studied. Several measures, such as the number of failures by some prespecified time, the number of errors remaining in the system at a future time, distribution of remaining number of faults in the system and reliability during a mission have been proposed in this research. Maximum likelihood estimation method was used to estimate the parameters. Sufficient conditions for the existence of roots of the ML equations were derived. Some of the important statistical aspects of G -0 and Yamada et al models, like conditions for the existence and uniqueness of the ML equations, were not worked out so far in the literature. We have derived these conditions and proved uniqueness of the roots for these models. Finally four different sets of actual failure time data were analyzed. i

An abstract specification language for Markov reliability models

Markov models can be used to compute the reliability of virtually any fault tolerant system. However, the process of delineating all of the states and transitions in a model of complex system can be devastatingly tedious and error-prone. An approach to this problem is presented utilizing an abstract model definition language. This high level language is described in a nonformal manner and illustrated by example

Automatic specification of reliability models for fault-tolerant computers

The calculation of reliability measures using Markov models is required for life-critical processor-memory-switch structures that have standby redundancy or that are subject to transient or intermittent faults or repair. The task of specifying these models is tedious and prone to human error because of the large number of states and transitions required in any reasonable system. Therefore, model specification is a major analysis bottleneck, and model verification is a major validation problem. The general unfamiliarity of computer architects with Markov modeling techniques further increases the necessity of automating the model specification. Automation requires a general system description language (SDL). For practicality, this SDL should also provide a high level of abstraction and be easy to learn and use. The first attempt to define and implement an SDL with those characteristics is presented. A program named Automated Reliability Modeling (ARM) was constructed as a research vehicle. The ARM program uses a graphical interface as its SDL, and it outputs a Markov reliability model specification formulated for direct use by programs that generate and evaluate the model

Bayesian inference for fault based software reliability models given software metrics data

We wish to predict the number of faults N and the time to next failure of a piece of software. Software metrics data are used to estimate the prior mean of N via a Poisson regression model. Given failure time data and a some well known fault based models for interfailure times, we show how to sample the relevant posterior distributions via Gibbs sampling using the package Winbugs. Our approach is illustrated with an example

Reliability models applicable to space telescope solar array assembly system

A complex system may consist of a number of subsystems with several components in series, parallel, or combination of both series and parallel. In order to predict how well the system will perform, it is necessary to know the reliabilities of the subsystems and the reliability of the whole system. The objective of the present study is to develop mathematical models of the reliability which are applicable to complex systems. The models are determined by assuming k failures out of n components in a subsystem. By taking k = 1 and k = n, these models reduce to parallel and series models; hence, the models can be specialized to parallel, series combination systems. The models are developed by assuming the failure rates of the components as functions of time and as such, can be applied to processes with or without aging effects. The reliability models are further specialized to Space Telescope Solar Arrray (STSA) System. The STSA consists of 20 identical solar panel assemblies (SPA's). The reliabilities of the SPA's are determined by the reliabilities of solar cell strings, interconnects, and diodes. The estimates of the reliability of the system for one to five years are calculated by using the reliability estimates of solar cells and interconnects given n ESA documents. Aging effects in relation to breaks in interconnects are discussed