Skip to main content

Scalability Analysis Tools

Project description

## Scalability Analysis Tools

[![Build Status](](
[![PyPI version](](

`sca_tools` analyzes software scalability benchmarking experiments,
specifically measurements of throughput as a function of applied load
or concurrency. It is a Python library and set of command line
utilities and that will help you:
- Model and quantify throughput bottlenecks in your application
- Capacity plan
- Compare performance benchmarks for regressions

We rely on Neil Gunther's
[Universal Scalability Law](
as a model and [lmfit]( to perform
model fitting. `sca_tools` differs from existing implementations (see
[Related Work](#related-work)) in a few ways:
- Emphasis on robust parameter estimation
- Support for propagation of parameter uncertanties when computing
derived quantities, such as latency, queue size, etc.
- Support for experimental measurement uncertainty
- Written using Scientific Python libraries

This is alpha software: use it at your **own risk** (e.g., don't use
it to make business decisions). It's very much a **work in progress**,
but currently includes:
- Fitting routines to the USL and basic reporting around
goodness-of-fit for USL's convention and coherence parameters.
- Graph outputs for best fit model, best fit model confidence
intervals, fit data, residuals, etc.
- Command line tools to produce these models from CSV data, as well
as manipulate and aggregate CSV data from computer experiments.

## Usage

The `fixtures/` directory contains the SPEC SDM91 load-througput
benchmark ported from Stefan Möding's
[R implementation]( of USL.

> python sca_tools/ --model_type usl fixtures/specsdm91.csv

----- Summary -----

[[Fit Statistics]]
# function evals = 41
# data points = 7
# variables = 3
chi-square = 27453.720
reduced chi-square = 6863.430
Akaike info crit = 63.920
Bayesian info crit = 63.758
lambda_: 89.9954927 +/- 14.21296 (15.79%) (init= 1000)
sigma_: 0.02772863 +/- 0.009121 (32.90%) (init= 0.1)
kappa: 0.00010437 +/- 1.99e-05 (19.04%) (init= 0.001)
[[Correlations]] (unreported correlations are < 0.100)
C(lambda_, sigma_) = 0.964
C(sigma_, kappa) = -0.467
C(lambda_, kappa) = -0.243


![Throughput model](docs/specsdm91-throughput_model.png)

## Related Work


## Citations

- Neil J. Gunther. *Guerrilla Capacity Planning: A Tactical Approach
to Planning for Highly Scalable Applications and
Services*. Springer, Heidelberg, Germany, 1st edition, 2007.
- Baron Schwartz. *Practical Scalability Analysis with the Universal
Scalability Law*. VividCortex, November 2015.

## License

Copyright © 2017 Bhaskar Mookerji

Distributed under the Apache License 2.0

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for sca-tools, version 0.1.2
Filename, size File type Python version Upload date Hashes
Filename, size sca_tools-0.1.2-py2.py3-none-any.whl (23.9 kB) File type Wheel Python version py2.py3 Upload date Hashes View
Filename, size sca_tools-0.1.2.tar.gz (37.2 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page