Abstract: Certain aspects of the present disclosure provide techniques for detecting and preventing web service usage anomalies. Embodiments include forecasting, based on a model, a number of resource instances for one or more web services for a time period. Embodiments include receiving, after the time period has elapsed, a detected number of resource instances for the one or more web services for the time period. Embodiments include comparing the detected number of resource instances to the forecasted number of resource instances. Embodiments include determining, based on the comparing, a usage anomaly. Embodiments include determining an impact of the usage anomaly. Embodiments include outputting an indication of the usage anomaly and the impact of the usage anomaly.

Abstract: An assessment of computing system performance may include evaluating, within a time window, the average latency of operations of a certain type and size with respect to a peer performance model constructed for operations of the same type and size. Such evaluation may result in the determination of a peer performance score. The peer performance score may be used to label performance characteristics of the computing system that are measured within the same time window. A library of such performance characteristics labeled with respective peer performance scores may be constructed by examining multiple computing systems, and such library may be used to construct one or more symptom models. Each symptom model may map a performance characteristic to a symptom severity score, which indicates the severity of a symptom of the computing system.

Abstract: Unexpected changes in application behavior are detected using time series telemetry in a manner that minimizes the upfront knowledge needed about the time series measurements themselves. A univariate analysis is performed on a set of operational metrics to identify anomalous signals indicative of potential anomalous events. A multivariate analysis is then performed to corroborate anomalous signals across groupings of metrics and to thereby determine that the anomalous signals correspond to an anomalous event. A boundary of the anomalous event is determined and the anomalous event is characterized based on a clustering of previously identified incidents. One or more participating entities are then determined for the anomalous event and a narrative description of the anomalous event is generated and presented to an end user. The narrative description identifies, among other things, the characterization of the anomalous event, a predicted cause of the anomalous event, and the participating entities.

Abstract: A display device failure detection method, device and system are disclosed. The display device failure detection method includes that: at least one detection picture image acquired by an image acquisition device when a target display device regularly displays a detection picture sequence is received, and at least one image to be detected is determined from the at least one detection picture image (S11); at least one region to be detected corresponding to at least one display region of the target display device in the at least one image to be detected is determined, and image data of the at least one region to be detected is processed to obtain at least one image processing result (S13); and whether a display exception occurs to the target display device is judged according to the image processing result (S15).

Abstract: A display device failure detection method, a display device failure detection device, a display device failure detection system and a computer-readable medium are disclosed provided. The display device failure detection method includes that: at least one detection picture image acquired by an image acquisition device when a target display device regularly displays a detection picture sequence is received, and at least one image to be detected is determined (S11); at least one region to be detected corresponding to at least one display region of the target display device in the at least one image to be detected is determined, and image data of the at least one region to be detected is processed to obtain at least one image processing result (S13); and whether a display exception occurs to the target display device is judged according to the image processing result (S15).

Abstract: Certain aspects of the present disclosure provide techniques for detecting and preventing web service usage anomalies. Embodiments include forecasting, based on a model, a number of resource instances for one or more web services for a time period. Embodiments include receiving, after the time period has elapsed, a detected number of resource instances for the one or more web services for the time period. Embodiments include comparing the detected number of resource instances to the forecasted number of resource instances. Embodiments include determining, based on the comparing, a usage anomaly. Embodiments include determining an impact of the usage anomaly. Embodiments include outputting an indication of the usage anomaly and the impact of the usage anomaly.

Abstract: An assessment of computing system performance may include evaluating, within a time window, the average latency of operations of a certain type and size with respect to a peer performance model constructed for operations of the same type and size. Such evaluation may result in the determination of a peer performance score. The peer performance score may be used to label performance characteristics of the computing system that are measured within the same time window. A library of such performance characteristics labeled with respective peer performance scores may be constructed by examining multiple computing systems, and such library may be used to construct one or more symptom models. Each symptom model may map a performance characteristic to a symptom severity score, which indicates the severity of a symptom of the computing system.