Intelligent Data Centres Issue 12 | Page 70

THE EDGE DATA CENTRE AND COLOCATION PROVIDERS MUST HAVE A SINGLE VIEW INTO THE FACILITY AND THE DATA THEY ARE HOLDING. 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. 70 Issue 12 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 an 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 trickier 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. ◊ www.intelligentdatacentres.com