A Compatible Hardware/Software Reliability Prediction Model.

Abstract

In this paper a new modeling methodology to characterize failure processes in Time-Sharing systems due to hardware transients and software errors is presented. The basic assumption made is that the instantaneous failure rate of a system resource can be approximated by a deterministic function of time plus a zero-mean stationary Gaussian process, both depending on the usage of the resource considered. The probability density function of the time to failure obtained under this assumption has a decreasing hazard function, partially explaining why other decreasing hazard function densities such as the Weibull fit experimental data so well. Furthermore, by considering the Operating System kernel as a system resource, this methodology sets the basis for independent methods of evaluating the contribution of software and hardware to system unreliability. The modeling methodology has been validated with the analysis of a real system. The predicted system behavior according to this methodology is compared with the predictions of other models such as the exponential, Weibull, and periodic failure rate. The implications of this methodology are discussed and some applications are given in the areas of Performance/Reliability modeling, software reliability evaluation, models incorporating permanent hardware faults, policy optimization, and design optimization. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jul 22, 1981
Accession Number
ADA113590

Entities

People

  • Thomas D. Smith
  • Xavier Castillo

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Advanced Electronics
  • C4I
  • Energy and Power Technologies
  • Space

DTIC Thesaurus Topics

  • Computational Science
  • Computer Programming
  • Computer Programs
  • Computers
  • Data Science
  • Databases
  • Gaussian Processes
  • Goodness Of Fit Tests
  • Information Processing
  • Information Science
  • Network Science
  • Operating Systems
  • Random Variables
  • Servomechanisms
  • Software Development
  • Stochastic Processes
  • System Software

Fields of Study

  • Engineering

Readers

  • Software Engineering.
  • Statistical inference.