optimal.scalability,USL-method {usl}

Point of optimal scalability of a USL model

Description

Calculate the point of optimal scalability for a specific model.

Usage

## S4 method for signature 'USL'
optimal.scalability(object, alpha, beta, gamma)

Arguments

object

A USL object.

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.

gamma

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

Details

The point of optimal scalability is defined as:

Nopt = \frac{1}{\alpha}

Below this point the existing capacity is underutilized. Beyond that point the effects of diminishing returns become visible more and more.

The value can be constructed graphically by projecting the intersection of the linear scalability bound and the Amdahl asymptote onto the x-axis.

The parameters alpha, beta and gamma 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.

The point of optimal scalability is undefined if alpha is zero.

This function accepts a arguments for beta and gamma although the values are not required to perform the calculation. This is on purpose to provide a coherent interface.

Value

A numeric value for the load where optimal scalability will be reached.

See Also

usl, peak.scalability,USL-method limit.scalability,USL-method

Examples

require(usl)

data(specsdm91)

optimal.scalability(usl(throughput ~ load, specsdm91))
## Optimal scalability will be reached at about 36 virtual users