Ca, Inc.

Michael Charles Johns

Peter William Mullarkey

Kevin Eugene Davis

A method for a diagnostic headline system using simple linguistic terms is described. The method comprises receiving historical and current performance data for a component of a system. An anomaly is determined in the current performance data by comparing the current performance data and the historical performance data for the component. The anomaly is determined whether or not it indicates an error in the system. The cause of the anomaly is determined. Steps for addressing the cause of the anomaly are recommended. A display is formatted of the current performance data, the error, and the recommended steps for addressing the cause of the anomaly.

Presenting diagnostic headlines using simple linguistic terms

A method for diagnosing system anomalies and presenting recommended solutions is described. The method comprises receiving current component performance data and historical component performance data regarding performance of a component of a multi-tier system. A baseline tier performance for each tier and a baseline component performance is determined. A cause of an anomaly in the performance of the component is determined by discovering deviations in current component performance data and current tier performance in compared to their baseline values. Based on these deviations, a recommended procedure for addressing the cause of the anomaly is determined. A display is formatted within a user interface of the recommended procedure for addressing the cause of the anomaly in the performance of the component.

Diagnosing anomalies based on deviation analysis

An anomaly detection method and system determine network status by monitoring network activity. A statistics based profile for said network over a period is generated to analyze potentially anomalous network activity to determine if said network activity is anomalous by comparing current activity against the profile. Using the profile as a reference, the anomaly detection system and process estimate and prioritize potentially anomalous network activity based on the probability that the behavior is anomalous. The level of severity that the anomaly detection process uses to determine if an alarm is needed is based on comparing user-adjustable thresholds to the current probability. If the threshold has been breached, the user is alerted, subject to other quality checks. After a reporting cycle concludes, the anomaly detection system and process recompiles the statistics based profile to take into account the information observed in the previous reporting cycle.

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Austin, TX, United States of America

Michael Charles Johns