Experts have been predicting for some time that the automation technologies that are applied in factories worldwide would be applied…

Experts have been predicting for some time that the automation technologies that are applied in factories worldwide would be applied to datacentres in the future. Not only to improve their efficiency but to help gather business insights from ever-increasing pools of data. The truth is that we’re rapidly advancing this possibility with the application of Robotic Process Automation (RPA) and machine learning in the datacentre environment. But why is this so important?

At the centre of digital transformation is data and thus, the datacentre. As we enter this new revolution in how businesses operate, it’s essential that every piece of data is handled and used appropriately to optimise its value. This is where the datacentre becomes crucial as the central repository for data. Not only are they required to manage increasing amounts of data, more complex machines and infrastructures, we also want them to be able to generate improved information about our data more quickly.

In this article, Matthew Beale, Modern Datacentre Architect at automation and infrastructure service provider, Ultima explains how RPA and machine learning are today paving the way for the autonomous datacentre.

The legacy datacentre

Currently, businesses spend too much time and energy on dealing with upgrades, patches, fixes and monitoring of their datacentres. While some may run adequately, most suffer from three critical issues;

•           Lack of consistent support, for example, humans make errors when updating patches or maintaining networks leading to compliance issues.

•           Lack of visibility for the business, for example, multiple IT staff look after multiple apps or different parts of the network with little coordination of what the business needs. 

•           Lack of speed when it comes to increasing capacity or migrating data or updating apps.

Human error is by far the most significant cause of network downtime. This is followed by hardware failures and breakdowns. With little to no oversight of how equipment is working, action can only be taken once the downtime has already occurred. The cost impact is much higher as the focus is taken away from other things to manage the cause of the issue, combined with the impact of the actual network downtime. Stability, cost and time management must be tightened to provide a more efficient datacentre. Automation can help achieve this.

‘Cobots’ make humans six times more productive

Automation provides ‘cobots’ to work alongside humans with unlimited benefits. The precisely structured environment of the datacentre is the perfect setting to deploy these software robots. There are many medial, repetitive and time intensive tasks that can be taken away from users and given to a software robot with the effect of boosting both consistency and speed.

Ultima calculates that the productivity ratio of ‘cobot’ to human is 6:1. By reviewing processes that are worth automating, software robots can be programmed, and once verified, they can repeat them every time. Whatever the process is, robotics ensure that it is consistent and accurate, meaning that every task will be much more efficient. This empowers teams to intervene only to make decisions in exceptional circumstances.

The self-healing datacentre

Automation minimises the amount of time that human maintenance of the datacentre is required. Robotics and machine learning restructures and optimises traditional processes, meaning that humans are no longer needed to perform patches to servers at 3 am. Issues can be identified and flagged by machines before they occur, eliminating downtime.

Re-distribution of resources and capacity management

As the lifecycle of an app across the business changes, resources need to be redeployed accordingly. With limited visibility, it’s extremely difficult, if not impossible, for humans to distribute resources effectively without the use of machines and robotics. For example, automation can increase or decrease resources accordingly towards the end of an app’s life to maximise resources elsewhere. Ongoing capacity management also evaluates resources across multiple cloud platforms for optimised utilisation. When the workload is effectively balanced, not only does this offer productivity cost savings, it also allows for predictive analytics.

The art of automation

These new, consumable automation functions are the result of what Ultima has already been doing for the last year when it found itself solving similar problems for three of its customers. It was moving three customers from their end of life 5.5 version of VMWare and recognised that it would be helpful to be able to automatically migrate them to the updated version, so it developed a solution to do this. Where once it would have taken 40 days to migrate workloads, the business cut that in half, resulting in a 33 per cent cost saving for those companies. It then moved on to looking at other processes to automate with the ambition of taking its customers on a journey to full datacentre automation.

Using discovery tools and automated scripts to capture all data required to design and migrate infrastructure to the automated datacentre, Ultima’s infrastructure is used as a code to create repeatable deployments, customised for customer environments. These datacentre deployments are then able to scale where needed without manual intervention.

The journey to a fully automated datacentre

The first level of automation provides information for administrators to take action in a user-friendly and consumable way, moving to a system that provides recommendations for administrators to accept actions based on usage trends. From there automation leads to a system that will automatically take remediation actions and raise tickets based on smart alerts. Then you move to a fully autonomous datacentre utilising AI & ML, which determines the appropriate steps and can self-learn and adjust thresholds.

AI-driven operations start with automation

Businesses are adopting modern ways of consuming applications as well as modern ways of working. Over 80 per cent of organisations are either using or adopting DevOps methodologies, and it is critical to the success of these initiatives that the platforms in place can support these ways of working while still keeping efficiency and utilisation high.

In the not too distant future is a central platform to support traditional and next-generation workloads which can be automated in a self-healing, optimum way at all times. This means that when it comes to migration, maintenance, upgrades, capacity changes, auditing, back-up and monitoring, the datacentre takes the majority of actions itself with no or little assistance or human intervention required. Similar to autonomous vehicles, the possibilities for automation are never-ending; it’s always possible to continually improve the way work is carried out.

Matthew Beale is Modern Datacentre Architect, Ultima, an automation and transformation partner. You can contact him at and visit Ultima at

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