Abstract: In some examples, a system identifies a potential issue based on comparing measurement data acquired at different hierarchical levels of a computing environment. Within a hierarchical level of the different hierarchical levels, the system determines, based on measurement data acquired for objects in the hierarchical level, whether the potential issue is localized to a subset of the objects.

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: In some examples, a system identifies a potential issue based on comparing measurement data acquired at different hierarchical levels of a computing environment. Within a hierarchical level of the different hierarchical levels, the system determines, based on measurement data acquired for objects in the hierarchical level, whether the potential issue is localized to a subset of the objects.

Abstract: In some examples, a system identifies a potential issue based on comparing measurement data acquired at different hierarchical levels of a computing environment. Within a hierarchical level of the different hierarchical levels, the system determines, based on measurement data acquired for objects in the hierarchical level, whether the potential issue is localized to a subset of the objects.

Abstract: In some examples, a system identifies a potential issue based on comparing measurement data acquired at different hierarchical levels of a computing environment. Within a hierarchical level of the different hierarchical levels, the system determines, based on measurement data acquired for objects in the hierarchical level, whether the potential issue is localized to a subset of the objects.

Abstract: In some examples, a system identifies a potential issue based on comparing measurement data acquired at different hierarchical levels of a computing environment. Within a hierarchical level of the different hierarchical levels, the system determines, based on measurement data acquired for objects in the hierarchical level, whether the potential issue is localized to a subset of the objects.

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.