usl (version 3.0.3)

predict,USL-method: Predict method for Universal Scalability Law models

Description

predict is a function for predictions of the scalability of a system modeled with the Universal Scalability Law. It evaluates the regression function in the frame newdata (which defaults to model.frame(object)). Setting interval to "confidence" requests the computation of confidence intervals at the specified level.

Usage

# S4 method for USL
predict(
  object,
  newdata,
  alpha,
  beta,
  interval = c("none", "confidence"),
  level = 0.95
)

Value

predict produces a vector of predictions or a matrix of predictions and bounds with column names fit, lwr, and

upr if interval is set to "confidence".

Arguments

object

A USL model object for which prediction is desired.

newdata

An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used.

alpha

Optional parameter to be used for evaluation instead of the parameter computed for the model.

beta

Optional parameter to be used for evaluation instead of the parameter computed for the model.

interval

Type of interval calculation. Default is to calculate no confidence interval.

level

Confidence level. Default is 0.95.

Details

The parameters alpha or beta are useful to do a what-if analysis. Setting these parameters override the model parameters and show how the system would behave with a different contention or coherency delay parameter.

predict internally uses the function returned by scalability,USL-method to calculate the result.

References

Neil J. Gunther. Guerrilla Capacity Planning: A Tactical Approach to Planning for Highly Scalable Applications and Services. Springer, Heidelberg, Germany, 1st edition, 2007.

See Also

usl, scalability,USL-method, USL-class

Examples

require(usl)

data(raytracer)

## Print predicted result from USL model for demo dataset
predict(usl(throughput ~ processors, raytracer))

## The same prediction with confidence intervals at the 99% level
predict(usl(throughput ~ processors, raytracer),
        interval = "confidence", level = 0.99)