Detecting and Diagnosing Anomalous Behavior in Large Systems with Change Detection Algorithms Large telecommunications networks are designed to achieve high reliability with hardware and software redundancy that is managed through complex faulttolerant mechanisms for error detection and recovery. Because of the fault tolerant mechanisms, when errors do occur they do not always cause failures and, hence, it can be difficult to detect anomalous behavior of the system and to determine its root cause. In this paper, using sequential system performance data, we present the application of multivariate change detection algorithms and visual analytics methods for detecting and diagnosing anomalous behavior with low latency in telecommunications systems. Such methods, coupled with domain knowledge, are efficient and effective for detecting and diagnosing anomalies as compared to log analysis. We demonstrate our methods with real data from a large system.

Analysis of Software Aging Impacts on Plant Anomaly Detection with Edge Computing Edge computing allows dataintensive analytic processes running on a cloud server to migrate to a local computing environment near the data sources, and as a result the architecture can benefit from a significant bandwidth reduction as well as improved security. However, due to the scarcity of resources on an edge device, a problem of software aging is likely to occur after longtime execution. The consequence of software aging should be significant, whereas such a risk has not been studied in the previous literature. In this paper, we propose a deterministic and stochastic Petri Net (DSPN) to quantitatively analyze the impacts of software aging phenomenon on a cyberphysical system using edge computing. As a data analytic application using realworld sensor data, we consider anomaly detection for a water treatment plant. We introduce two applicationoriented performance measures, namely detection loss probability and the expected number of false alarms in terms of plant anomaly detection. Our numerical and simulation experiments on the proposed DSPN show that software aging clearly has negative impacts on the defined performance measures that reveal the necessity of greater attention to software aging issues in edge computing systems.