Evaluation of A Resilience Embedded System Using Probabilistic Model-Checking
This work addresses the difficulty of evaluating resilience strategies for embedded systems in non-human environments, but it is incremental as it applies an existing method (probabilistic model-checking) to a specific new strategy.
The paper tackles the problem of evaluating a new resilience strategy for embedded systems that automatically resets a Micro Processor Unit (MPU) to prevent freezing or malfunctioning from external noise, using probabilistic model-checking to show cost-effective and reliable evaluation with metrics like system failure reduction and Average Down Time (ADT).
If a Micro Processor Unit (MPU) receives an external electric signal as noise, the system function will freeze or malfunction easily. A new resilience strategy is implemented in order to reset the MPU automatically and stop the MPU from freezing or malfunctioning. The technique is useful for embedded systems which work in non-human environments. However, evaluating resilience strategies is difficult because their effectiveness depends on numerous, complex, interacting factors. In this paper, we use probabilistic model checking to evaluate the embedded systems installed with the above mentioned new resilience strategy. Qualitative evaluations are implemented with 6 PCTL formulas, and quantitative evaluations use two kinds of evaluation. One is system failure reduction, and the other is ADT (Average Down Time), the industry standard. Our work demonstrates the benefits brought by the resilience strategy. Experimental results indicate that our evaluation is cost-effective and reliable.