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

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