Investigating the Relationship between Processor and Memory Reliability in Data Science: A Bivariate Model Approach

Hanan Haj Ahmad (), Ehab M. Almetwally and Dina A. Ramadan
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Hanan Haj Ahmad: Department of Basic Science, Preparatory Year Deanship, King Faisal University, Hofuf 31982, Al Ahsa, Saudi Arabia
Ehab M. Almetwally: Faculty of Business Administration, Delta University for Science and Technology, Gamasa 11152, Egypt
Dina A. Ramadan: Department of Mathematics, Faculty of Science, Mansoura University, Mansoura 35516, Egypt

Mathematics, 2023, vol. 11, issue 9, 1-23

Abstract: Modeling the failure times of processors and memories in computers is crucial for ensuring the reliability and robustness of data science workflows. By understanding the failure characteristics of the hardware components, data scientists can develop strategies to mitigate the impact of failures on their computations, and design systems that are more fault-tolerant and resilient. In particular, failure time modeling allows data scientists to predict the likelihood and frequency of hardware failures, which can help inform decisions about system design and resource allocation. In this paper, we aimed to model the failure times of processors and memories of computers; this was performed by formulating a new type of bivariate model using the copula function. The modified extended exponential distribution is the suggested lifetime of the experimental units. It was shown that the new bivariate model has many important properties, which are presented in this work. The inferential statistics for the distribution parameters were obtained under the assumption of a Type-II censored sampling scheme. Therefore, point and interval estimation were observed using the maximum likelihood and the Bayesian estimation methods. Additionally, bootstrap confidence intervals were calculated. Numerical analysis via the Markov Chain Monte Carlo method was performed. Finally, a real data example of processors and memories failure time was examined and the efficiency of the new bivariate distribution of fitting the data sample was observed by comparing it with other bivariate models.

Keywords: data science; computer processor; bivariate models; copula; modified extended exponential; Markov Chain Monte Carlo; maximum likelihood estimation; Bayesian estimation; bootstrap; simulation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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