用自适应算法追踪软件老化，应对负载变化带来的检测失效问题。
Adaptive Detection of Software Aging under Workload Shift
- 引入ADWIN等自适应检测机制，动态响应负载变化
- 在突发、渐进、重复负载切换下F1分数均超0.93
- 适合长期运行系统故障预警，尤其负载多变场景
软件老化会影响长时间运行的系统，导致性能逐步退化并增加故障风险。为缓解此问题，本文提出一种基于机器学习的自适应检测方法，适用于动态负载环境。我们对比了静态模型与集成自适应检测器的模型，其中自适应检测器采用原始用于概念漂移的Drift Detection Method（DDM）和Adaptive Windowing（ADWIN）。在模拟的突发、渐进及重复性负载转换实验中，静态模型在未见过的负载配置下性能显著下降；而采用ADWIN的自适应模型在所有分析场景中均保持高精度，F1分数超过0.93。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。