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This paper explores the dependability and availability of programmable devices (PDs) with controlled multilevel degradation using Markov models. It presents a classification of single-fragment and multi-fragment models based on key parameters influencing failure rates and reconfiguration capabilities for PDs. The study develops a Markov availability model for PDs, detailing state transition matrices and simulation methods in MATLAB. Three PD models are considered: a single-fragment model, a multi-fragment model with degradation, and a multi-fragment model with degradation and reconfiguration. The simulation results demonstrate how reconfiguration and degradation affect system availability over extended operational periods. A comparison of availability functions across the models illustrates the effects of degradation and reconfiguration, delivering key findings for improving PD resilience in critical applications within harsh settings.

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Models for Assessing the Dependability of Programmable Devices with Controlled Multi-Level Degradation

  • 会议论文

  • 发表日期：

    2025年

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  • Vyacheslav Kharchenko,
  • Yuriy Ponochovnyi,
  • Oleksandr Vdovichenko,
  • Khanlar Mahmudov

摘要

This paper explores the dependability and availability of programmable devices (PDs) with controlled multilevel degradation using Markov models. It presents a classification of single-fragment and multi-fragment models based on key parameters influencing failure rates and reconfiguration capabilities for PDs. The study develops a Markov availability model for PDs, detailing state transition matrices and simulation methods in MATLAB. Three PD models are considered: a single-fragment model, a multi-fragment model with degradation, and a multi-fragment model with degradation and reconfiguration. The simulation results demonstrate how reconfiguration and degradation affect system availability over extended operational periods. A comparison of availability functions across the models illustrates the effects of degradation and reconfiguration, delivering key findings for improving PD resilience in critical applications within harsh settings.