Abstract: An adaptive mechanism is provided that learns the response time characteristics of a workload by measuring the response times of end user transactions, classifies response times into buckets, and dynamically adjusts the response time distribution as response time characteristics of the workload change. The adaptive mechanism maintains the actual distribution across changes and, thus, helps the end user to understand changes of workload behavior that take place over a longer period of time. The mechanism is stable enough to suppress spikes and returns a constant view of workload behavior, which is required for long term, performance analysis and capacity planning. The mechanism distinguishes between an initial learning phase of establishing the distribution and one or multiple reaction periods. The reaction periods can be for example a fast reaction period for strong fluctuations of the workload behavior and a slow reaction period for small deviations.

Abstract: An adaptive mechanism is provided that learns the response time characteristics of a workload by measuring the response times of end user transactions, classifies response times into buckets, and dynamically adjusts the response time distribution as response time characteristics of the workload change. The adaptive mechanism maintains the actual distribution across changes and, thus, helps the end user to understand changes of workload behavior that take place over a longer period of time. The mechanism is stable enough to suppress spikes and returns a constant view of workload behavior, which is required for long term, performance analysis and capacity planning. The mechanism distinguishes between an initial learning phase of establishing the distribution and one or multiple reaction periods. The reaction periods can be for example a fast reaction period for strong fluctuations of the workload behavior and a slow reaction period for small deviations.

Abstract: An adaptive mechanism is provided that learns the response time characteristics of a workload by measuring the response times of end user transactions, classifies response times into buckets, and dynamically adjusts the response time distribution as response time characteristics of the workload change. The adaptive mechanism maintains the actual distribution across changes and, thus, helps the end user to understand changes of workload behavior that take place over a longer period of time. The mechanism is stable enough to suppress spikes and returns a constant view of workload behavior, which is required for long term, performance analysis and capacity planning. The mechanism distinguishes between an initial learning phase of establishing the distribution and one or multiple reaction periods. The reaction periods can be for example a fast reaction period for strong fluctuations of the workload behavior and a slow reaction period for small deviations.

Abstract: An adaptive mechanism is provided that learns the response time characteristics of a workload by measuring the response times of end user transactions, classifies response times into buckets, and dynamically adjusts the response time distribution as response time characteristics of the workload change. The adaptive mechanism maintains the actual distribution across changes and, thus, helps the end user to understand changes of workload behavior that take place over a longer period of time. The mechanism is stable enough to suppress spikes and returns a constant view of workload behavior, which is required for long term, performance analysis and capacity planning. The mechanism distinguishes between an initial learning phase of establishing the distribution and one or multiple reaction periods. The reaction periods can be for example a fast reaction period for strong fluctuations of the workload behavior and a slow reaction period for small deviations.