Proactive avoidance of performance issues in computing environments using a probabilistic model and causal graphs
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United States of America
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Oct 29, 2024
Grant Date -
Apr 6, 2023
app pub date -
May 19, 2022
filing date -
May 19, 2022
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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.
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