Abstract: A system and method for selecting forecasting performance metrics from among a plurality of performance metrics includes a data mining kernel that receives performance metrics from a collection agent. The data mining kernel performs a factor analysis on the performance metrics to identify one or more factors and associates each performance metric with a corresponding one of the one or more factors. For each of the one or more factors, the data mining kernel selects the performance metrics having a weight greater than a predetermined value thus pairing the selected performance metrics down to the forecasting performance metrics. The forecasting performance metrics are then stored in a database for access by a capacity planning system.

Abstract: A system and method for extracting and forecasting computing resource data such as workload consumption of mainframe computing resources using an autoregressive model. The system and method forecast mainframe central processing unit (CPU) consumption with ninety-five percent accuracy using historical performance data. The system and method also provide an upper ninety-five percent confidence level and a lower ninety-five percent confidence level. The system and method retrieve performance records from a computer platform in one second intervals, statistically collapses the one second performance data into fifteen minute performance data, statistically collapses the fifteen minute performance data into one week performance data, and generates a time series equivalent to collecting performance data at one week intervals.

Abstract: A system and method for selecting forecasting performance metrics from among a plurality of performance metrics includes a data mining kernel that receives performance metrics from a collection agent. The data mining kernel performs a factor analysis on the performance metrics to identify one or more factors and associates each performance metric with a corresponding one of the one or more factors. For each of the one or more factors, the data mining kernel selects the performance metrics having a weight greater than a predetermined value thus pairing the selected performance metrics down to the forecasting performance metrics. The forecasting performance metrics are then stored in a database for access by a capacity planning system.

Abstract: A system and method for extracting and forecasting computing resource data such as workload consumption of mainframe computing resources using an autoregressive model. The system and method forecast mainframe central processing unit (CPU) consumption with ninety-five percent accuracy using historical performance data. The system and method also provide an upper ninety-five percent confidence level and a lower ninety-five percent confidence level. The system and method retrieve performance records from a computer platform in one second intervals, statistically collapses the one second performance data into fifteen minute performance data, statistically collapses the fifteen minute performance data into one week performance data, and generates a time series equivalent to collecting performance data at one week intervals.

Abstract: A system and method for processing performance metric data and converting the data from Universal/Uniform Data Format (UDF) to a form readable by data analysis/reporting tools such as SAS IT Service Vision. Performance metric data is collected by collection agents in UDF files. Universal/Uniform Data Format files produced by the same type of collection agent are reformatted and mapped to a dataset having a number of records or observations. The datasets are sorted by grouping the records according to a characteristic such as an attribute and performance data tables are constructed from the sorted datasets in the form of SAS datasets. The SAS datasets may be read by data analysis/reporting tools that use the datasets to produce charts and graphs of computer system performance for display.