Hybrid Automated Reliability Predictor Research Articles

An integrated reliability modeling environment

Abstract In this paper, we propose an integrated reliability/availability modeling and analysis environment suitable for heterogeneous hierarchical system analysis. A key component of this environment is a high level system specification and input language which acts as a common interface to several off-the-shelf reliability modeling and analysis tools (Koren JM, Gaertner J. CAFTA: a fault tree analysis tool designed for PSA. In: Proc. of Probabilistic Safety Assessment and Risk Management: PSA '87, Zurich, Switzerland, vol. 2, 1987:588–592; Sahner RA, Trivedi KS. Reliability modeling using SHARPE, IEEE Trans. Reliability R-36, 2 June 1987:186–193; Ciardo G, Muppala J, Trivedi K. SPNP: Stochastic Petri Net Package. In: Proc. 3rd Int. Workshop on Petri Nets and Performance Models, Kyoto, Japan, Dec. 1989:142–151; Smotherman MK, Dugan JB, Trivedi KS, Geist RM. The hybrid automated reliability predictor. AIAA Journal of Guidance, Control and Dynamics May/June 1986:319–331). The complexity of the underlying solvers for reliability analysis is kept transparent to the user by the design of a common language and translators to these tools. This design has several advantages including interoperability, ability of the interpreter to choose a particular solver depending on the problem characteristics and the ease of enhancing the system capability transparent to the user. The technique, being very compact in model description, also leads to a quick analysis of the design, guiding the designers at every stage for reliable product development.

Fault trees and Markov models for reliability analysis of fault-tolerant digital systems

Reliability analysis of fault tolerant computer systems for critical applications is complicated by several factors. In this paper, we discuss these modeling difficulties and describe and demonstrate approaches to handling them. Three important techniques characterize our approach. First, behavioral decomposition separates the system failure modes specification from the recovery process specification. Second, a fault tree representation of the system failure modes is converted to an equivalent Markov model, to which the recovery models are added automatically. Third, the fault tree to Markov chain conversion allows the definition of new dynamic fault tree gates to capture the sequence dependent failure modes that are often associated with advanced fault tolerant systems. Two advanced fault tolerant computer systems are described, and fault tree models for their analysis are presented. HARP (the Hybrid Automated Reliability Predictor) is a software package developed at Duke University and NASA Langley Research Center that is used to analyze the example systems.

Dynamic fault-tree models for fault-tolerant computer systems

Reliability analysis of fault-tolerant computer systems for critical applications is complicated by several factors. Systems designed to achieve high levels of reliability frequently employ high levels of redundancy, dynamic redundancy management, and complex fault and error recovery techniques. This paper describes dynamic fault-tree modeling techniques for handling these difficulties. Three advanced fault-tolerant computer systems are described: a fault-tolerant parallel processor, a mission avionics system, and a fault-tolerant hypercube. Fault-tree models for their analysis are presented. HARP (Hybrid Automated Reliability Predictor) is a software package developed at Duke University and NASA Langley Research Center that can solve those fault-tree models.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Reliability estimation of fault-tolerant systems: tools and techniques

A comparative evaluation of state-of-the-art tools and techniques for estimating the reliability of fault-tolerant computing systems is presented. The theory of reliability estimation is briefly reviewed. Five current approaches are compared in detail: HARP (hybrid automated reliability predictor), SURE (semi-Markov unreliability range estimator), HEIRESS (hierarchical estimation of interval reliability by skewed sampling), SHARPE (symbolic hierarchical automated reliability and performance evaluator), and SAVE (system availability estimator). Particular attention is given to design limitations imposed by underlying model assumptions, on the one hand, and the efficiency and accuracy of the solution techniques employed, on the other hand. >

Analysis of Typical Fault-Tolerant Architectures using HARP

HARP (the Hybrid Automated Reliability Predictor) is a software package that implements advanced reliability modeling techniques. We present an overview of some of the problems that arise in modeling highly reliable fault-tolerant systems; the overview is loosely divided into model construction and model solution problems. We then describe the HARP approach to these difficulties, which is facilitated by a technique called behavioral decomposition. The bulk of this paper presents examples of the dependability evaluation of some typical fault-tolerant systems, including a local-area network, two well-known fault-tolerant computer systems (C.mmp and SIFT), and an example of a flight control system. HARP has been used to solve very large models. A system consisting of 20 components distributed among 7 stages produced a Markov chain with 24 533 states and over 335 000 transitions (without coverage). Depending on the system used to run this example, the run time took anywhere from 4 to 8 hours. HARP is undergoing beta testing at approximately 20 sites. It is written in standard FORTRAN 77, consists of nearly 30000 lines of code and comments, and has been tested under several operating systems. The graphics interface (written in C) runs on an IBM PC AT, and produces text files that can be used to solve the system on the PC (for very small systems), or can be uploaded to a larger machine. HARP is accompanied by an Introduction and Guide for Users. For information on obtaining a copy of HARP, contact one of the authors.

The hybrid automated reliability predictor

In this paper, we present an overview of the hybrid automated reliability predictor (HARP), under development at Duke and Clemson Universities. The HARP approach to reliability prediction is characterized by a decomposition of the overall model into distinct fault-occurrence/repair and fault /error-handling submodels. The faultoccurrence/repair model can be cast as either a fault tree or as a Markov chain and is solved analytically. Both exponential and Weibull time to failure distributions are allowed. There are a variety of choices available for the specification of the fault/error-handling behavior that may be solved analytically or simulated. Both graphical and textual interfaces are provided to HARP.

Hybrid reliability modeling of fault-tolerant computer systems

Current technology allows sufficient redundancy in fault-tolerant computer systems to insure that the failure probability due to exhaustion of spares is low. Consequently, the major cause of failure is the inability to correctly detect, isolate, and reconfigure when faults are present. Reliability estimation tools must be flexible enough to accurately model this critical fault-handling behavior and yet remain computationally tractable. This paper discusses reliability modeling techniques based on a behavioral decomposition that provides tractability by separating the reliability model along temporal lines into nearly disjoint fault-occurrence and fault-handling submodels. An Extended Stochastic Petri Net (ESPN) model provides the needed flexibility for representing the fault-handling behavior, while a nonhomogeneous Markov chain accounts for the possibly non-Poisson fault-occurrence behavior. Since the submodels are separate, the ESPN submodel, in which all time constants are of the same order of magnitude, can be simulated. The nonhomogeneous Markov chain is solved analytically, and the result is a hybrid model. The method of coverage factors, used to combine the submodels, is generalized to more accurately reflect the fault-handling effectiveness within the fault-occurrence model. However, due to approximations made in the aggregation of the two submodels and inaccurate estimation of component failure rates and other model parameters, errors can still arise in the subsequent reliability predictions. The accuracy of the model predictions is evaluated analytically, and error bounds on the system reliability are produced. These modeling techniques have been implemented in the HARP (Hybrid Automated Reliability Predictor) program.