Technologies such as Augmented Reality and Artificial Intelligence have the potential to add huge value to data centre teams. But it’s important that these technologies are properly understood ahead of making big investments. Robert Neave, CTO and Co-founder, Nlyte Software, tells us how AR and AI are set to shape the future of DCIM.
The data centre is a mighty force in the global economy, helping brands deliver cloud and Internet-based services to consumers at a huge scale. With growing scale and increasing consumer demands for faster, more reliable services, the pressure has been on the IT function to optimise, rationalise and monetise at pace.
Yet the same pressures face the team in streamlining the data centre delivery of business services and increasing demands for reliability and uptime cannot be met through incremental technology change alone.
This is where new, but tragically misunderstood technologies like AI and Augmented Reality (AR) can add value. However, it’s best to thoroughly understand the way the technologies add value and have a firm grip on what they offer – and what’s just hype – before you make big investments.
The temptation is there since these technologies hold out the offer of creating efficiencies in data centre optimisation for energy use, staff time in diagnosing and fixing faults (and regularly scheduled maintenance) and keeping to customer SLAs. By adding automation to the mix, most organisations are betting that they can supercharge their IT teams to work faster and smarter without burning them out.
Augmented Reality – a new way of seeing
Augmented Reality (AR) made a big splash in the consumer space with Pokémon GO – but like all technologies that start within the consumer realm, it soon made itself useful in the enterprise world too. Why are companies like Microsoft and Snap betting big on these solutions? Beyond cool use cases for gamers, the technology holds considerable promise for anyone working in complex environments.
While the thought of lens-bedecked workers might conjure up a vision of warehouse workers, the truth is that anyone working with time, space and complexity issues could do with a fairy godmother helping them out. That’s the promise of Augmented Reality – literally showing the path or the next step to take in any procedural situation.
The applications for mentoring, guiding and simply speeding up complex tasks are enormous, and could easily span whole categories like teaching, surgery, engine repair, boundary surveying, or even secure crowd control.
Technology that changes the way humans interact with software for the physical management of data centre assets and the location of logical workloads would be a massive boon to assisting staff in a hurry to meet SLAs or to guide newer joiners unsure of the right process.
An augmented view of current and future data centre capacity provides greater future-proofing and the ability to both troubleshoot on the fly as well as strategically plan with all the required information right in front of users’ eyes.
Simply put, AR helps with instructing remote personnel to make changes and allow managers to see what they can see. AR also helps reduce response times to change, risks, travel costs and carbon impact too.
Artificial Intelligence – a new way to apply critical thinking
AI is beginning to power some DCIM solutions in a few key areas:
Targeting: Data centre and colocation providers must have a single view into the facility and the data they are holding. Such a granular level of visibility requires DCIM and there are smart ways that AI can make this process smoother, by making decisions and actions for the human user to get to an optimal state quicker. Such a solution will address data centre analytics by collecting, normalising and creating patterns of facilities and IT data and streaming it to a control centre.
The solution then uses its Machine Learning (ML) capabilities to extract predictive models to send the analysis back to a visual dashboard to display the potential vulnerabilities, such as future hot server rows or under-utilised racks.
What’s the difference between Machine Learning and AI? Machine Learning is the part that looks for patterns and AI is the automatic decision-maker that acts on what is identified within these patterns. Often it is sensible to refer to both together as part of the same solution, e.g. ML/AI.
Together they can help with:
Collecting: Capturing data from all distributed silos such as servers, sensors, HVAC, building monitoring software, PDUs, processors and many other points.
Analysing: Advanced content analytics enable facility managers to understand not just what happened, but also how and why.
Actioning: Refining data into a visual state so team members may quickly comprehend current conditions as well as increase operational efficiencies and cost savings.
Efficiency: DCIM not only allows facility managers to target the data they hold, it can also help mine additional environmental data to help run the facility better and more efficiently. This is the reason why many data centre and colocation providers are turning to DCIM powered by ML/AI for the everyday running of their facilities.
The technology can improve efficiency by using algorithms which collect data from thousands of sensors all around the network which in turn feed into a ML/AI system that is modelled on neurons found in the human brain. The system then analyses the broad range of indicators, from energy consumption levels to safety constraints, in order to identify the best course of action.
This makes the process more unified as ML/AI is creating a cohesive platform for the infrastructure strategy by incorporating the processes, tools and workforce focusing on end-to-end solutions allowing initiatives to work together by design to reduce duplicate effort.
For example, by recording the airflow, the system can identify if any of the air filters are clogged then notify the team and in turn push the air through less clogged filters until they are changed. Once changed, the system would resume service as usual.
Security: ML/AI can be constantly monitoring every part of the network. This gives managers the means of dealing with the growing influx of data while learning and adapting to overcome new, never-seen-before malware while recognising suspicious user behaviours and detecting anomalous network traffic. It can also collect and analyse forensic data, scan code and infrastructure for vulnerabilities, potential weaknesses and configuration errors, making it one of the most powerful tools at security’s disposal.
Implementing such ML/AI solutions with DCIM can reduce reliance on human intervention by reducing the man-hours spent on round-the-clock monitoring and decreasing the risk of human error in response.
The future is just around the corner
A future of ML/AI and AR everything is around the corner but needs to be carefully applied so that the enormous investments in data centre uptime are maintained. AR is a non-disruptive technology that augments the human’s ability to work smarter and faster.
ML/AI is a slightly more tricky concept – and may well be applied in smaller projects as the industry learns just how best to use it. It is easy to imagine broader use cases emerging as successes snowball – and if that occurs those who lead in early adoption may find massive gains put them well ahead of their peers.