Abstract: A system and method of altering computer resource allocation may include receiving a first time-series metric describing a processes; receiving a second time-series resource metric describing a computer resource; and analyzing the first time-series metric and the second time-series resource metric as an independent and a dependent variable to determine a gradient coefficient defining the ratio of the rate of change between the first time-series metric and the second time-series resource metric. The result may be used to predict resource usage, and to provision or allocate the resource accordingly.

Abstract: A system and method of altering computer resource allocation may include receiving a first time-series metric describing a processes; receiving a second time-series resource metric describing a computer resource; and analyzing the first time-series metric and the second time-series resource metric as an independent and a dependent variable to determine a gradient coefficient defining the ratio of the rate of change between the first time-series metric and the second time-series resource metric. The result may be used to predict resource usage, and to provision or allocate the resource accordingly.

Abstract: Detection of anomalous events in the operation of information technology (IT) components includes receiving messages, which describe events in the operation of the IT components in real time, and categorizing and condensing the messages received in a first time interval into message patterns by message pattern type. Based on a distribution of occurrences of the message patterns in the first time interval and in preceding time intervals, anomaly scores are assigned to the message patterns, and one or more of the message patterns are classified as being anomalous message patterns that correspond to potentially anomalous events in the operation of the IT infrastructure installation. A degree of correlation between occurrences of the anomalous message patterns and occurrences of application alarms is determined. Message patterns with high anomaly scores and having a high degree of correlation with application alarms are deemed significant and prioritized for display to users.

Abstract: Detection of anomalous events in the operation of information technology (IT) components includes receiving messages, which describe events in the operation of the IT components in real time, and categorizing and condensing the messages received in a first time interval into message patterns by message pattern type. Based on a distribution of occurrences of the message patterns in the first time interval and in preceding time intervals, anomaly scores are assigned to the message patterns, and one or more of the message patterns are classified as being anomalous message patterns that correspond to potentially anomalous events in the operation of the IT infrastructure installation. A degree of correlation between occurrences of the anomalous message patterns and occurrences of application alarms is determined. Message patterns with high anomaly scores and having a high degree of correlation with application alarms are deemed significant and prioritized for display to users.

Abstract: The system may include a troubleshooting activity recorder configured to record troubleshooting sessions. Each troubleshooting session may include a sequence of queries and query results. The troubleshooting activity recorder may include a query transformer configured to transform the queries and the query results into transformed queries and transformed query results before recording the troubleshooting sessions. The troubleshooting activity recorder may be configured to record the transformed queries and the transformed query results as troubleshooting session information in a troubleshooting activity database. The system may include a troubleshooting pattern learning unit including a graph builder configured to generate a troubleshooting pattern graph having query nodes and links between the query nodes based on the troubleshooting session information.

Abstract: The system may include a troubleshooting activity recorder configured to record troubleshooting sessions. Each troubleshooting session may include a sequence of queries and query results. The troubleshooting activity recorder may include a query transformer configured to transform the queries and the query results into transformed queries and transformed query results before recording the troubleshooting sessions. The troubleshooting activity recorder may be configured to record the transformed queries and the transformed query results as troubleshooting session information in a troubleshooting activity database. The system may include a troubleshooting pattern learning unit including a graph builder configured to generate a troubleshooting pattern graph having query nodes and links between the query nodes based on the troubleshooting session information.