On our first day as Turbonomic employees, our team had some great discussions with CTO Charles Crouchman about Turbonomic, ParkMyCloud, and the market for infrastructure automation tools. Charles explained his vision of the future of infrastructure automation, which parallels the automation trajectory that cars and other vehicles have been following for decades. It’s a comparison that’s useful in order to understand the goals of fully-automated cloud infrastructure – and the mindset of cloud users adopting this paradigm. (And of course, given our name, we’re all in on driving analogies!)

The Five Levels of Vehicle Autonomy

The idea of the five levels of vehicle autonomy – or six, if you include level 0 – is an idea that comes from the Society of Automotive Engineers.

The levels are as follows:

  • Level 0 – No Automation. The driver performs all driving tasks with no tools or assistance.
  • Level 1 – Driver Assistance. The vehicle is controlled by the driver, but the vehicle may have driver-assist features such as cruise control or an automated emergency brake.
  • Level 2 – Partial Automation or Occasional Self-Driving. The driver must remain in control and engaged in driving and monitoring, but the vehicle has combined automated functions such as acceleration and steering/lane position.
  • Level 3 – Conditional Automation or Limited Self-Driving. The driver is a necessity, but not required to monitor the environment. The vehicle monitors the road and traffic, and informs the driver when he or she must take control.
  • Level 4 – High Automation or Full Self-Driving Under Certain Conditions. The vehicle is capable of driving under certain conditions, such as urban ride-sharing, and the driver may have the option to control the vehicle. This is where airplanes are today – for the most part, they can fly themselves, but there’s always a human pilot present.
  • Level 5 – Full Automation or Full Self-Driving Under All Conditions. The vehicle can drive without a human driver or occupants under all conditions. This is an ideal, but right now, neither the technology nor the people are ready for this level of automation.

How These Levels Apply to Infrastructure Automation Tools

Now let’s take a look at how these levels apply to infrastructure automation tools and infrastructure:

  • Level 0 – No Automation. No tools in place.
  • Level 1 – Driver Assistance. Some level of script-based automation with limited applications, such as scripting the installation of an application so it’s just one user command, instead of hand-installing it.
  • Level 2 – Partial Automation or Occasional Self-Driving. In cloud infrastructure, this translates to having a monitoring system in place that can alert you to potential issues, but cannot take action to resolve those issues.
  • Level 3 – Conditional Automation or Limited Self-Driving. Think of this as traditional incident resolution or traditional orchestration. You can build specific automations to handle specific use cases, such as opening a ticket in a service desk, but you have to know what the event trigger is in order to automate a response.
  • Level 4 – High Automation or Full Self-Driving Under Certain Conditions. This is the step where analytics are integrated. A level-4 automated infrastructure system uses analytics to decide what to do. A human can monitor this, but is not needed to take action.
  • Level 5 – Full Automation or Full Self-Driving Under All Conditions. Full automation. Like in the case of vehicles, both the technology and the people are a long way from this nirvana.

So where are most cloud users in the process right now? There are cloud users and organizations all over this spectrum, which makes sense when you think about vehicle automation: there are early adopters who are perfectly willing to buy a Tesla, turn on auto-pilot, and let the car drive them to their destination. But, there are also plenty of laggards who are not ready to take their hands off the wheel, or even turn on cruise control.

Most public cloud users have at least elements of levels 1 and 2 via scripts and monitoring solutions. Many are at level 3, and with the most advanced platforms, organizations reach level 4. However, there is a barrier between levels 4 and 5: you will need an integrated hardware/software solution. The companies that are closest to full automation are the hyperscale cloud companies like Netflix, Facebook, and Google who have basically built their own proprietary stack including the hardware. This is where Kubernetes comes from and things like Netflix Scryer.

In our conversation, Charles said: “The thing getting in the way is heterogeneity, which is to say, most customers buy their hardware from one vendor, application software from another company, storage from another, cloud capacity from another, layer third-party software applications in there, use different development tools –– and none of these things were effectively built to be automated. So right now, automation needs to happen from outside the system, with adaptors into the systems. To get to level 5, the automation needs to be baked in from the system software through the application all the way up the stack.”

What Defines Early Adopters of Infrastructure Automation Tools

While there’s a wide scale of adoption in the market right now, there are a few indicators that can predict whether an organization or an individual will be open to infrastructure automation tools.

The first is a DevOps approach. If an organization using DevOps, they have already agreed to let software automate deployments, which means they’re accepting of automation in general – and likely to be open to more.

Another is whether resource management is centralized within the organization or not. If it is centralized, the team or department doing the management tends to be more open to automation and software solutions. If ownership is distributed throughout the organization, it’s naturally more difficult to make unified change.

Ultimately, the goal we should all be striving for is to use infrastructure automation tools to step up the levels of automated resource configuration and cost control. Through automation, we can reduce management time and room for human error to achieve optimized environments.