Performance Model Estimation and Tracking Using Optimal Filters

To update a performance model, its parameter values must be updated, and in some applications (such as autonomic systems) tracked continuously over time. Direct measurement of many parameters during system operation requires instrumentation which is impractical. Kalman filter estimators can track such parameters using other data such as response times and utilizations, which are readily observable. This paper adapts Kalman filter estimators for performance model parameters, evaluates the approximations which must be made, and develops a systematic approach to setting up an estimator. The estimator converges under easily verified conditions. Different queueing-based models are considered here, and the extension for state-based models (such as stochastic Petri nets) is straightforward.