Abstract: Proactive avoidance of performance issues in computing environments. In one embodiment, a causal dependency graph representing the usage dependencies among the various components of a computing environment is formed, the components being associated with key performance indicators (KPIs). A probabilistic model is trained with prior incidents that have occurred in the components to correlate outliers of KPIs in associated components to prior incidents. The training includes determining the correlation based on the causal dependency graph. Upon detecting the occurrence of outliers for performance metrics, an imminent performance issue likely to occur in a specific component is identified based on the probabilistic model and the detected outliers. A preventive action is performed to avoid the occurrence of the imminent performance issue in the specific component.

Abstract: Proactive avoidance of performance issues in computing environments. In one embodiment, a causal dependency graph representing the usage dependencies among the various components of a computing environment is formed, the components being associated with key performance indicators (KPIs). A probabilistic model is trained with prior incidents that have occurred in the components to correlate outliers of KPIs in associated components to prior incidents. The training includes determining the correlation based on the causal dependency graph. Upon detecting the occurrence of outliers for performance metrics, an imminent performance issue likely to occur in a specific component is identified based on the probabilistic model and the detected outliers. A preventive action is performed to avoid the occurrence of the imminent performance issue in the specific component.

Abstract: An aspect of the present disclosure facilitates accelerating outlier prediction of performance metrics in performance managers deployed in new computing environments. In one embodiment, a digital processing system receives an input data specifying a business vertical to which a new computing environment is directed, a performance metric of interest, and a computing component of the new computing environment for which the performance metric is sought to be measured. In response, the system selects, from a set of prediction models, a prediction model for the performance metric, based on the input data. The selected prediction model is then used in a performance manager to predict outliers for the performance metric of interest during operation of the new computing environment.