OpenStack is the leading open cloud platform, and Ubuntu is the world’s most popular operating system for OpenStack. Over the past two years we have created a tool that allows users to build an Ubuntu OpenStack cloud on their own hardware in a few simple steps: Autopilot.

This post covers the design process we followed on our journey from alpha to beta to release.

Alpha release: getting the basics right

We started by mapping out a basic Autopilot journey based on stakeholder requirements and designed a first cut of all the necessary steps to build a cloud:

  1. Choose the cloud configuration from a range of OpenStack optionsChoose cloud configuration
  1. Select the hardware the cloud should be built on
    Select the hardware
  1. View deployment status while the cloud is being built
    View deployment status
  1. Monitor the status and usage of the cloud
    Monitor Cloud

After the initial design phase Autopilot was developed and released as an alpha and a beta. This means that for over a year, there was a product to play around with, test and improve before it was made generally available.

Beta release: feedback and improvements

Providing a better overview: increased clarity in the dashboard

Almost immediately after the engineering team started building our new designs, we discovered that we needed to display an additional set of data on the storage graphs. On top of that, some guerilla testing sessions with Canonical engineers brought to light that the CPU and the storage graphs were easily misinterpreted.


After some more competitive research and exploratory sketching, we decided to merge the graphs for each section by putting the utilisation on a vertical axis and the time on the horizontal axis. This seemed to improve the experience for our engineers, but we also wanted to validate with users in usability testing, so we tested the designs with eight participants that were potential Autopilot users. From this testing we learned to include more information on the axes and to include detailed information on hover.

The current graphs are quite an evolution compared to what we started with:
Improved dashboard graphs

Setting users up for success: information and help before the process begins

Before a user gets to the Autopilot wizard, they have to configure their hardware, install an application called MAAS to register machines and install Landscape to get access to Autopilot. A third tool called Juju is installed to help Autopilot behind the scenes.

All these bits of software work together to allow users to build their clouds; however, they are all developed as stand-alone products by different teams. This means that during the initial design phase, it was a challenge to map out the entire journey and get a good idea of how the different components work together.

Only when the Autopilot beta was released, was it finally possible for us to find some hardware and go through the entire journey ourselves, step by step. This really helped us to identify common roadblocks and points in the journey where more documentation or in-app explanation was required.

Increasing transparency of the process: helping users anticipate what they need and when configuration is complete

Following our walk-through, we identified a number of points in the Autopilot journey where contextual help was required. In collaboration with the engineering team we gathered definitions of technical concepts, technical requirement, and system restrictions.

Autopilot walk-through

Based on this info, we made adjustments to the UI. We designed a landing page  with a checklist and introduction copy, and we added headings, help text, and tooltips to the installation and dashboard page. We also included a summary panel on the configuration page, to guide users through the journey and provide instant feedback.


GA release: getting Autopilot ready for the general public

Perhaps the most rewarding type of feedback we gathered from the beta release — our early customers liked Autopilot but wanted more features. From the first designs Autopilot has aimed to help users quickly set up a test cloud. But to use Autopilot to build a production cloud, additional features were required.

Testing without the hardware: try Autopilot on VMware

One of the biggest improvements for GA release was making it easy to try Autopilot, even for people that don’t have enough spare hardware to build a cloud. Our solution: try Autopilot using VMware!

Supporting customisation:  user-defined roles for selected hardware

In the alpha version a user could already select nodes, but in most enterprises users want more flexibility. Often there are different types of hardware for different roles in the cloud, so users don’t always want to automatically distribute all the OpenStack services over all the machines. We designed the ability to choose specific roles like storage or compute for machines, to allow users to make the most of their hardware.

Machine roles

Allowing users more control: a scalable cloud on monitored hardware

The first feature we added was the ability to add hardware to the cloud. This makes it possible to grow a small test cloud into a production sized solution. We also added the ability to integrate the cloud with Nagios, a common monitoring tool. This means if something happens on any of the cloud hardware, users would receive a notification through their existing monitoring system.


The benefits of early release

This month we are celebrating another  release of OpenStack Autopilot. In the two years since we started designing Autopilot, we have been able to add many improvements and it has been a great experience for us as designers to contribute to a maturing product.

We will continue to iterate and refine the features that are launched and we’re currently mapping the roadmap for the months ahead. Our goal remains for Autopilot to be a tool for users to maintain and upgrade an enterprise grade cloud that can be at the core of their operations.