Abstract: An autonomic computing system for dynamically managing a performance model for a data center. Measured performance data for a data processing system and performance model performance data from a performance model of the data processing system are obtained from which is generated estimated performance data. Upon comparison, if a difference between the measured performance data and the estimated performance data falls within defined limits, the performance module in the performance model structure is identified as an accurate model. If the difference does not fall within defined limits, the estimated performance data, the model performance data, and the measured performance data is analyzed to estimate new performance parameters for the performance model structure. Responsive to estimating new performance parameters for the performance model structure, the performance model structure is updated with the estimated new performance parameters.

Abstract: A method of determining scenario causality, along with precedence causality, is disclosed. Information is recorded relating to events occurring during execution of a process. The information includes object related information and process related information. The information is translated into a sequence of scenario graph language statements, one or more events translated to a statement. From the statements, process execution flow is determined establishing some scenario causality and precedence causality.

Abstract: A method of determining scenario causality, along with precedence causality, is disclosed. Information is recorded relating to events occurring during execution of a process. The information includes object related information and process related information. The information is translated into a sequence of scenario graph language statements, one or more events translated to a statement. From the statements, process execution flow is determined establishing some scenario causality and precedence causality.