By Ajit Kumar Verma, Srividya Ajit, Visit Amazon's Manoj Kumar Page, search results, Learn about Author Central, Manoj Kumar,
The size of dependability attributes on actual platforms is a truly time-consuming and expensive affair, making analytical or simulation modeling the one attainable options. Dependability of Networked Computer-based platforms explores reliability, availability and safeguard modeling of networked computer-based structures utilized in life-critical purposes resembling avionics, nuclear strength crops, autos and chemical procedure industries.
Dependability of Networked Computer-based Systems offers an summary of easy dependability modeling strategies and addresses new demanding situations in dependability modeling of networked computer-based platforms, in addition to new tendencies, their functions and barriers. It covers various dependability modeling methods:
- stochastic processes,
- Markov and semi-Markov models,
- response-time distribution,
- stochastic Petri-net-based modeling formalisms, and
- Monte Carlo simulation models.
Dependability of Networked Computer-based Systems presents scholars and researchers with an in depth review of dependability types and research options. working towards engineers also will locate this article an invaluable consultant to decision-making in keeping with procedure dependability on the layout, operation and upkeep levels.
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Additional info for Dependability of Networked Computer-based Systems
Consider a sequence of n independent Bernoulli trials with probability of success equal to p on each trial. Let Yn denote the number of successes in n trials. The domain of the random variable Yn is all the n-tuples of 00 s and 10 s, and the image is f0; 1; . ; ng. The value assigned to an n-tuple by Yn simply corresponds to the number of 10 s in the n-tuple. 7 Random Variables 23 pk ¼ PðYn ¼ kÞ & C ðn; kÞpk qnÀk ; for 0 k n ¼ 0; otherwise ð2:32Þ The above equation gives the probability of k ‘successes’ in n independent trials, where each trial has probability p of success.
In: Proceedings of 28th Int. Symp. on Fault Tolerant Computing 304–313 26. Tomek L, Mainkar V, Geist RM, Trivedi KS (1994) Reliability modeling of life-critical, realtime systems. Proceedings of the IEEE 82:108–121 27. In: Proceeding of 7th International Conference on Real-time Computing System and Applications 279–286 28. Mainkar V, Trivedi KS (1994) Transient analysis of real-time systems using deterministic and stochastic petri nets. In: Int’l Workshop on Quality of Communication-Based Systems 29.
0g from ft ¼ Tn g onwards depends on the past fZðtÞ; 0 t Tn g only through Yn . Let vi;j ðtÞ ¼ PrfZ ðtÞ ¼ jjY0 ¼ ig. The matrix V ðtÞ ¼ vi;j ðtÞ is referred to as conditional transient probability matrix of MRGPs. The following theorem gives the generalized Markov renewal equation satisfied by VðtÞ. For the sake of conciseness, ðK Ã VÞðtÞ to denote the matrix whose element ði; jÞ is defined as follows: t XZ dki;h ðuÞvh;j ðt À uÞ ð3:26Þ ðk Ã vÞi;j ðtÞ ¼ h2S 0 Means, ðK Ã VÞðtÞ is a matrix of functions of t whose generic element is obtained as the row by column convolution of the matrix KðtÞ and matrix VðtÞ.