Probabilistic modeling of hardware and software interactions for system reliability assessment

Abstract

An important aspect of complex hardware-software systems reliability is the interaction between hardware and software. Most of the existing work has assumed either independence between hardware (HW) and software (SW) or a fixed proportion of hardware reported failures to represent HW/SW interactions. These assumptions do not necessarily reflect reality. In this paper, probabilistic HW/SW interactions, in conjunction with hardware and software reliability, are considered to model and assessment overall hardware-software systems reliability. However, incorporating uncertainty demands the use of stochastic programming to estimate the parameters of the hardware reliability model. To capture the HW/SW interactions, a Markov Stochastic process model with uncertainty in transition rates is proposed and solved using Monte Carlo sampling. By combining hardware, software, and probabilistic HW/SW interactions, the overall system reliability can be predicted not only as a point estimator but as quantiles or confidence intervals. The proposed methodology was demonstrated with data from a real computer system provided by Los Alamos National Laboratory. Reliability predictions for a 1,095-day horizon were carried out with an R script in approximately 1.5 s running on a laptop, which demonstrates the feasibility and convenience of the approach. In the four cases analyzed, the 97.5% lower bound of system reliability estimation was above the reliability calculated assuming independent hardware and software reliability.

Keywords:

• Hardware
• mathematical programming
• reliability
• software
• stochastic processes

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Alex Davila-Frias

Alex Davila-Frias earned his Ph.D. degree in Industrial and Manufacturing Engineering at North Dakota State University in 2021. He has seven years of professional experience primarily in a supplier company of electronic devices for General Motors Ecuador, as quality engineer and head of supplier’s quality and environmental testing laboratory. In 2014 he joined Escuela Politécnica Nacional in Ecuador, where he is currently an associate professor. His research interest is focused on quality, reliability, operations management, and machine learning. He is a member of IISE.

Nita Yodo

Nita Yodo is an assistant professor in the Department of Industrial and Manufacturing Engineering at North Dakota State University (NDSU). She is also a founder and the director of the NDSU Advanced System Engineering Laboratory (ASEL) and a former director of the NDSU Center of Quality, Reliability and Maintainability Engineering (CQRME). Her research interest includes engineering system design for reliability, failure resilient, and sustainability. These endeavors have garnered substantial funding from a diverse array of state and federal agencies, including the NSF, ND-EPSCoR, NDSC, NDCC, and numerous others.

Om Prakash Yadav

Om Prakash Yadav is the Professor and Chair of the Dept. of Industrial and Systems Engineering at North Carolina A&T State University. He completed his Ph.D. in Industrial Engineering at Wayne State University. He has published over 150 research papers in the areas of quality, reliability, product development, and operations management. He is a Fellow of Institute of Industrial and Systems Engineers (IISE).

Phattara Khumprom

Phattara Khumprom earned his Ph.D. degree in Industrial Engineering from North Dakota State University, USA, in 2022. He is currently a full-time tenure track lecturer in Logistics and Supply Chain Management under the Graduate School of Management and Innovation (GMI) at King Mongkut’s University of Technology Thonburi (KMUTT), Bangkok, Thailand. His current research area of interest is regarding deep learning for Prognostics and Health Management (PHM) applications and predictive analysis for complex engineered systems. He is a member of IISE and IEEE.