Making apples to apples comparisons between different cloud providers is very difficult, because each one offers instances with varying vCPUs, RAM, SSD space and HDD space.
As an attempt to provide a clearer price comparison between compute service prices, I've used the multiple linear regression to "normalise" the pricing of on-demand, general purpose compute instances across different cloud providers.
In essence, If every cloud provider offered the same size compute instances, how expensive would they be?
I'll be taking the price tables of:
- Google Cloud - Predefined machine types
- AWS - On demand instances
- Azure - Linux virtual machines
and converting them into the instance sizes offered by Catalyst Cloud. You can find the datasets and their sources here. We won't be taking into account term or volume discounts.
I've used the scikit-learn library's multiple linear regression to achieve the desired normalisation, and Pandas for managing the data.
You can see my working on an IPython notebook here.
Included with this repo is a python script that makes it easier to display the results of this graph. It is mostly only intended for administrators of the Catalyst Cloud website, in order to provide up-to-date data on the price comparison page.
The Python script converts the Jupyter notebook to regular Python, runs that code, converts the CSV outputs into an HTML graph, and optionally deploys that graph to an OpenStack bucket.
A Dockerfile is also included to make it easier to manage the awkward dependencies required to convert a Jupyter notebook into usable python.
To build the docker image, run:
cd catalystcloud-price-comparison
docker build -t price-comparison .
To run the docker container, run:
docker run -it --rm -v <PATH TO YOUR OPEN_RC FILE DIRECTORY>:/home/open_rc_files price-comparison
To run the deploy script within the docker container, source the relevant open_rc file, then run:
cd /home
python deploy_graph.py --help
This will provide you with the scripts documentation. As an example, to deploy
the standard graph to the container named price-comparison, run:
python deploy_graph.py --fresh-data --deploy price-comparison site