Software aging is a phenomenon that affects long-running systems, leading to progressive performance degradation and increasing the risk of failures. To mitigate this problem, this work proposes an adaptive approach based on machine learning for software aging detection in environments subject to dynamic workload conditions. We evaluate and compare a static model with adaptive models that incorporate adaptive detectors, specifically the Drift Detection Method (DDM) and Adaptive Windowing (ADWIN), originally developed for concept drift scenarios and applied in this work to handle workload shifts. Experiments with simulated sudden, gradual, and recurring workload transitions show that static models suffer a notable performance drop when applied to unseen workload profiles, whereas the adaptive model with ADWIN maintains high accuracy, achieving an F1-Score above 0.93 in all analyzed scenarios.

翻译：软件老化是一种影响长期运行系统的现象，会导致性能逐渐下降并增加故障风险。为缓解此问题，本研究提出一种基于机器学习的自适应方法，用于在动态工作负载条件下检测软件老化。我们评估并比较了静态模型与包含自适应检测器的自适应模型，这些检测器具体包括漂移检测方法（DDM）和自适应窗口（ADWIN），它们最初为概念漂移场景开发，在本工作中被应用于处理工作负载变化。通过对模拟的突发、渐进和周期性工作负载转换进行实验，结果表明静态模型在应用于未见工作负载模式时性能显著下降，而采用ADWIN的自适应模型在所有分析场景中均保持高准确率，F1分数超过0.93。