A Comparative Analysis of Software Aging in Image Classifiers on Cloud and Edge

Image classifiers for recognizing real-world objects are widely used in the Internet of Things (IoT) and Cyber-Physical Systems(CPSs). A classifier is trained offline by machine learning algorithms with training data sets, and then it is deployed on a cloud or an edge computing system for online label predictions. As the classifier's performance depends on the underlying software infrastructure, it may degrade over time due to software faults causing software aging. In this paper, we address this issue and experimentally investigate software aging observed in an image classification system that continuously runs on cloud and edge computing environments. We apply several statistical techniques to analyze degradation trends in the systems under stress tests. Our statistical trend analysis confirms the degradation trends in the throughput as well as the available memory resources both in the cloud and the edge environments. Contrary to our expectation, the edge computing environment under test had much less impact on the performance degradation than our cloud environment when the workload is high, although the latter one has four times larger allocated memory resources. We also show that the observed performance degradation trends are associated with the memory usage of specific processes by performing correlation analysis.