This tutorial describes how to use nonlinear regression techniques to fit the parameters of any kind of performance model to performance data measured at the boundaries of the system. The advantage of this approach, which has never been a standard practice in performance work, is that it avoids the need for intrusive monitoring of execution paths, such as profiling.   The topics covered will include:   1. The estimation problem   2. Regression basics: normal equations, confidence intervals   3. Non-linear regression using iteration   4. Fitting a performance model into non-linear regression   5. Significance of model details (pruning insignificant details)   6. Examples
['C. Murray Woodside']
Regression techniques for performance parameter estimation