Objective Long-running software systems suffer from aging, characterized by rising performance degradation, resource depletion, and elevated failure rates. Conventional rejuvenation techniques, which often depend on predetermined restart intervals, model the system as simply healthy, deteriorated, or failed. However, practical evidence from a variety of fields indicates that wear and defect buildup frequently accelerate nonlinearly, necessitating more adaptable mathematical explanations.Method Compared to simpler two-parameter or linear models, an extended three-parameter power-law model which consists of a scaling factor, exponent, and offset is employed here to more accurately depict this behavior. The proposed framework enables adaptive rejuvenation policies triggered by observed conditions rather than rigid schedules by continuously monitoring a degradation metric tuned to this model. Renewal theory with rewards can be used to enhance rejuvenation timing and analytically evaluate steady-state unavailability.Results By accurately simulating real-world dynamics, numerical results show that these degradation-aware, threshold-based policies outperform fixed interval approaches by accurately modelling real-world dynamics, particularly in scenarios with increasing aging.Conclusions Together, the extended degradation model and the proposed rejuvenation policies provide a unified analytical framework that improves system availability and reduces long-run operational costs. This study shows that alert-based strategies consistently outperform risk-based policies because they allow for early, data-driven maintenance across various software aging conditions.
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