Detection of suspicious time windows in memory monitoring

Modern memory monitoring tools do not only offer analyses at a single point in time, but also offer features to analyze the memory evolution over time. These features provide more detailed insights into an application's behavior, yet they also make the tools more complex and harder to use. Analyses over time are typically performed on certain time windows within which the application behaves abnormally. Such suspicious time windows first have to be detected by the users, which is a non-trivial task, especially for novice users that have no experience in memory monitoring. In this paper, we present algorithms to automatically detect suspicious time windows that exhibit (1) continuous memory growth, (2) high GC utilization, or (3) high memory churn. For each of these problems we also discuss its root causes and implications. To show the feasibility of our detection techniques, we integrated them into AntTracks, a memory monitoring tool developed by us. Throughout the paper, we present their usage on various problems and real-world applications.