摘要:
A system and method are provided for determining problem conditions in an IT infrastructure using aggregate anomaly analysis. The anomalies in the metrics occurring in the monitored IT infrastructure are aggregated from all resources reporting metrics as a function of time. The aggregated metric anomalies are then normalized to account for the state of the monitored IT infrastructure to provide a normalized aggregate anomaly count. A threshold noise level is then determined utilizing a variably selectable desired level of confidence such that a problem event is only determined to likely be occurring in the IT infrastructure when the normalized aggregate anomaly count exceeds the threshold noise level. The normalized aggregate anomaly count is monitored against the threshold noise level as a function of time, such that a problem event in the IT infrastructure is identified when the normalized aggregate anomaly count exceeds the threshold noise level at a given time.
摘要:
A system and method for correlating fingerprints in an Information Technology (IT) infrastructure for automated intelligence, where a fingerprint provides an indication of the activity and operation of the IT infrastructure immediately preceding an event. It is determined whether a correlation exists between multiple fingerprints to determine whether such fingerprints separately indicate the occurrence of the event for the same reason. If a degree of match is found to exist between the rule sets of multiple fingerprints that exceeds a certain threshold, the fingerprints are determined to indicate the occurrence of the event for the same reason and the rule sets for those fingerprints can be merged together with the probabilities that such rules will indicate the occurrence of the event adjusted accordingly. In one or more embodiments, the fingerprint matching correlation procedures are implemented to account for time or phase shifts between the rule sets in two fingerprints.
摘要:
An integrity management system predicts abnormalities in complex systems before they occur based upon the prior history of abnormalities within the complex system. A topology of the nodes of a complex system is generated and data is collected from the system based on predetermined metrics. In combination with dynamic thresholding, fingerprints of the relevant nodes within a complex system at various time intervals prior to the occurrence of the abnormality are captured and weighted. The fingerprints can then be applied to real-time data provide alerts of potential abnormality prior to their actual occurrence.
摘要:
Methods and systems for determination of thresholds for time-series data. Data is transformed by reducing outliers, dividing the time series data into discrete time intervals, and taking parts of the data corresponding to the range that the thresholds will bound. If data cycles are known, they may be applied to the data and the resulting sets are weighted. Thresholds are then derived from the weighted means and variances of the sets of weighted data.
摘要:
An integrity management system predicts abnormalities in complex systems before they occur based upon the prior history of abnormalities within the complex system. A topology of the nodes of a complex system is generated and data is collected from the system based on predetermined metrics. In combination with dynamic thresholding, fingerprints of the relevant nodes within a complex system at various time intervals prior to the occurrence of the abnormality are captured and weighted. The fingerprints can then be applied to real-time data provide alerts of potential abnormality prior to their actual occurrence.
摘要:
An integrity management system predicts abnormalities in complex systems before they occur based upon the prior history of abnormalities within the complex system. A topology of the nodes of a complex system is generated and data is collected from the system based on predetermined metrics. In combination with dynamic thresholding, fingerprints of the relevant nodes within a complex system at various time intervals prior to the occurrence of the abnormality are captured and weighted. The fingerprints can then be applied to real-time data provide alerts of potential abnormality prior to their actual occurrence.