Patent application number | Description | Published |
20090177924 | CONTEXT SENSITIVE DETECTION OF FAILING I/O DEVICES - Methods for context sensitive detection of failing I/O devices sample and record a response time of an I/O device for each of a first plurality of time intervals to generate a first plurality of sampled and recorded response times, and to determine whether or not at least one I/O error has occurred in each of the first plurality of time intervals. A mathematical model is applied which characterizes the first plurality of sampled and recorded response times. The mathematical model is applied in accordance with an I/O device category corresponding to the I/O device. The mathematical model provides a frame of reference for defining an I/O failure. | 07-09-2009 |
20100174947 | DAMAGED SOFTWARE SYSTEM DETECTION - 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. | 07-08-2010 |
20120096320 | SOFT FAILURE DETECTION - A method, system, and computer program product detect soft failures as follows. A set of artifacts being generated by at least one process in a system is monitored. A number of artifacts being generated by the process is determined to be below a given threshold in response to the monitoring. The process is monitored in response to the determination. A current state of the process is determined in response to the analyzing. A notification is generated in response to the current state of the process including a set of abnormal behaviors. | 04-19-2012 |
20120123991 | METHOD FOR DETERMINING A PREFERRED NODE IN A CLASSIFICATION AND REGRESSION TREE FOR USE IN A PREDICTIVE ANALYSIS - 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. | 05-17-2012 |
20120209568 | MULTIPLE MODELING PARADIGM FOR PREDICTIVE ANALYTICS - 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. | 08-16-2012 |
20120265723 | DETERMINING WHEN TO CREATE A PREDICTION BASED ON DELTAS OF METRIC VALUES - 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. | 10-18-2012 |
20130061095 | SOFTWARE FAILURE DETECTION - A method detects soft failures as follows. A set of artifacts being generated by at least one process in a system is monitored. A number of artifacts being generated by the process is determined to be below a given threshold in response to the monitoring. The process is monitored in response to the determination. A current state of the process is determined in response to the analyzing. A notification is generated in response to the current state of the process including a set of abnormal behaviors. | 03-07-2013 |
20130086431 | MULTIPLE MODELING PARADIGM FOR PREDICTIVE ANALYTICS - 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. | 04-04-2013 |
20130091385 | USER-COORDINATED RESOURCE RECOVERY - A computing system resource recovery method can include identifying a resource manager associated with a computing transaction, classifying the computing transaction to determine a predetermined metric, measuring an actual metric of the computing transaction, comparing the predetermined metric to the actual metric to detect abnormal behavior in the transaction and modeling the abnormal behavior to determine how the resource manager is affected by the abnormal behavior. | 04-11-2013 |
20130091391 | USER-COORDINATED RESOURCE RECOVERY - 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. | 04-11-2013 |
20130097109 | METHOD FOR DETERMINING A PREFERRED NODE IN A CLASSIFICATION AND REGRESSION TREE FOR USE IN A PREDICTIVE ANALYSIS - 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. | 04-18-2013 |