Analyze system scalability with the Universal Scalability Law

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This is an R package to analyze system performance data with the Universal Scalability Law.

The Universal Scalability Law (USL) was developed by Dr. Neil J. Gunther. It can be used to analyze system performance data in order to learn more about the scalability limitations of the system.

Details are presented in the book Guerrilla Capacity Planning and on the authors website.


Here is an example for the scalability analysis of a Sun SPARCcenter 2000 in the SPEC SDM 91 benchmark. The data used is available for download from the SPEC website and also included as a demo dataset.


# Load data from the SPEC SDM91 benchmark


# Analyze "throughput" by "load" for the "specsdm91" data
usl.model <- usl(throughput ~ load, specsdm91)

# Show a model summary including scalability coefficients

# Predict the location of the maximum in the scalability function

# Plot original data and computed scalability function
plot(specsdm91, pch=16)
plot(usl.model, col="red", add=TRUE)

The summary command returns the following output:

usl(formula = throughput ~ load, data = specsdm91)

Scale Factor for normalization: 64.9

   Min     1Q Median     3Q    Max
0.1214 0.2254 0.3966 0.7799 1.0000

   Min     1Q Median     3Q    Max
-70.89 -23.59  19.39  86.14 274.88

       Estimate Std. Error t value Pr(>|t|)
sigma 1.705e-02  3.318e-03   5.137  0.00365 **
kappa 7.892e-05  2.492e-05   3.167  0.02489 *
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 140.6 on 5 degrees of freedom
Multiple R-squared: 0.9624,  Adjusted R-squared: 0.9549

The following image shows the plotted output:

SPEC SDM91 scalability function

SPEC SDM91 scalability function


The package is available from CRAN. Use the following command to install the package from the repository:



In addition to the package documentation there is also a package vignette available. Install the package and use the following command to open the vignette:


The vignette is also available from CRAN: