Adaptive Detection of Software Aging under Workload Shift

2025-11-05

AI Summary

To address the challenge of effectively detecting software aging under dynamic workloads, this paper proposes a machine learning–based detection method incorporating a concept drift adaptation mechanism. For the first time, online concept drift detectors—specifically ADWIN and DDM—are integrated into software aging monitoring, coupled with sliding-window-based tracking of performance metrics to enable real-time responsiveness to abrupt, gradual, and periodic workload transitions. Unlike static models, the proposed approach significantly enhances robustness and generalizability in aging identification for long-running systems. Experimental results demonstrate that the ADWIN-adaptive model achieves a consistently high F1-score above 0.93 across diverse unseen workload scenarios, markedly outperforming conventional methods. This work establishes a scalable, adaptive paradigm for aging-aware early warning in high-reliability systems.

Technology Category

• Machine Learning: Hardware-aware ML
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Multiagent Systems: Teamwork

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Models for Web evolution

Abstract

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.

Problem

• Detecting software aging under dynamic workload conditions
• Comparing adaptive models with static detection approaches
• Maintaining high accuracy during workload transitions using ADWIN

Innovation

• Adaptive machine learning for aging detection
• Using ADWIN and DDM for workload shifts
• Maintains high accuracy across workload transitions