RPA and machine learning brings us the autonomous data centre

RPA and machine learning brings us the autonomous data centre

With a need to deliver business and customer value faster than before, many enterprises are taking their agile software development practices to the next level and adopting DevOps methodologies as well as microservices. Critical to the success of these initiatives are platforms that can support these ways of working whilst still keeping efficiency and utilisation high.

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 Data Centre Architect at automation and infrastructure service provider, Ultima explains how RPA and machine learning are today paving the way for the autonomous data centre.

Why do we need an autonomous 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.Without cost-effective storage and increasingly powerful hardware, digital transformation and the new business models associated with it wouldn’t be possible.

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. 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.

The legacy data centre

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 data centre. Automation can help achieve this.

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The journey to a fully automated data centre

Full data centre automation is rather like moving from a manual drive car to driver assistance and then to a fully autonomous ‘driverless’ car. Currently, humans manage, monitor and operate the data centre which requires manual tooling and thresholding. This is extremely labour intensive and often requires tweaking infrastructure to deal with unexpected issues.

The journey to a fully automated data centre varies according to the type and the individual intricacies of an organisation. However, within the next two years we can expect to see many businesses, especially in fast moving sectors, either already there or on their way to having a fully autonomous data centre.

There are various levels of achievable automation that can occur in the data centre to move it on from current manual systems:

Assisted action: The first step along the journey provides information for administrators to take action in a user-friendly and consumable way, such as centralised logins. It can also ensure high availability by retrieving backups if something fails. The process essentially replaces the administrator hitting the ‘go’ button.

Partial automation: This step moves to a system that provides recommendations for administrators to accept actions based on usage trends. Using Dynamic Resource Scheduling (DRS) the system looks at trends on performance and which areas are getting particularly busy so that it can distribute resources to ensure an even balance, resulting in better performance. This can be especially effective for billing or HR payroll systems which tend to peak at the end of the month.

Conditional automation : This leads to a system using modern technology that will automatically take remediation actions and raise tickets based on smart alerts. For example, the system looks at security information and event management to collate lots of information from lots of different data points, such as user logins and the data being accessed. Machine learning algorithms will take this information and compare it with historical usage data to identify trends. Based on these metrics it will take action if it believes an account has been compromised.

Fully autonomous : Utilising Artificial Intelligence (AI) and Machine Learning (ML), the autonomous data centre determines the appropriate steps and can self-learn and adjust thresholds when needed to allow for efficient storage that delivers cost savings. It can plan ahead by modelling scenarios based on current and future usage patterns and make changes depending on how much storage a particular project needs.

Benefits of the fully automated data centre

One major benefit of automation is the introduction of the self-healing data centre. 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. Automation minimises the amount of time that human maintenance of the data centre is required.

Another benefit is efficient resource planning 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. 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.

AI-driven operations start with automation

In the next two years we’ll begin to see data centres supporting 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 data centre takes the majority of actions itself with no or little assistance or human intervention required.

Whatever the process within the data centre is, automation robots ensure that it is consistent and accurate, meaning that every task will be much more efficient. Ultima calculates that the productivity ratio of an automation ‘cobot’ to human is 6:1, empowering teams to intervene only to make decisions in exceptional circumstances. This means that the type of operational effort requirement from humans changes from ensuring that something happens and fixing problems, to querying the business and spending time developing applications and platforms.

Similar to autonomous vehicles, the possibilities for automated data centres are never-ending; it’s always possible to continually improve the way work is carried out.

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