Abstract: A computing system includes a processor configured to identify a resource manager associated with a computing transaction, classify the computing transaction to determine a predetermined metric, measure an actual metric of the computing transaction, compare the predetermined metric to the actual metric to detect abnormal behavior in the transaction and model the abnormal behavior to determine how the resource manager is affected by the abnormal behavior.

Abstract: Techniques are described for determining what node of a classification and regression tree (CART) should be used by a predictive analysis application. A first approach is to use a standard deviation of the data at a given the level of the CART to determine whether data in the next, lower node is more consistent than the data in the current node. A second approach is to measure a correlation between data points in a given node and the time at which each point was sampled (or other correlation metric) to identify a preferred node.

Abstract: A computing system includes a processor configured to identify a resource manager associated with a computing transaction, classify the computing transaction to determine a predetermined metric, measure an actual metric of the computing transaction, compare the predetermined metric to the actual metric to detect abnormal behavior in the transaction and model the abnormal behavior to determine how the resource manager is affected by the abnormal behavior.

Abstract: In an embodiment, deltas are calculated between respective current metric values for respective entities and previous metric values for the respective entities. A subset of the deltas is determined. A sum of the subset is calculated, and the sum is divided by a number of the subset to create an average delta for the subset. If one of the respective entities has one of the deltas that is greater than or equal to the average delta for the subset and the one of the respective entities was not previously used to create the previous prediction, then a current prediction is created.

Abstract: Techniques are described for monitoring a performance metric. A multiple modeling approach is used to improve predictive analysis by avoiding the issuance of warnings during spikes which occur as a part of normal system processing. This approach increases the accuracy of predictive analytics on a monitored computing system, does not require creating rules defining periodic processing cycles, reduces the amount of data required to perform predictive modeling, and reduces the amount of CPU required to perform predictive modeling.

Abstract: Damage to a software stack accessed by multiple application programs can be detected by monitoring the rate of stack failures at the stack via a stack monitor and comparing the rate of stack failures with a time related threshold. An alarm is generated when the rate of stack failures exceeds the time related threshold.

Abstract: Techniques are described for determining what node of a classification and regression tree (CART) should be used by a predictive analysis application. A first approach is to use a standard deviation of the data at a given the level of the CART to determine whether data in the next, lower node is more consistent than the data in the current node. A second approach is to measure a correlation between data points in a given node and the time at which each point was sampled (or other correlation metric) to identify a preferred node.

Abstract: A computer implemented method for a computer including a processor having a software stack accessed by multiple application programs includes receiving software requests from the multiple applications at the software stack; monitoring the rate of stack failures at the stack via a stack monitor; comparing the rate of stack failures with a time related threshold; and generating an alarm when the rate of stack failures exceeds the time related threshold.