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challenging when data centre teams don ’ t always have a clear understanding of exactly how their rooms are performing from a cooling , capacity and power perspective . Indeed , when faced with an external issue – such as an increased thermal demand placed on facilities by a surge in hosted services – the default position for many operations teams remains to just keep throwing more cooling at the problem . This simply adds to the data centre ’ s overall carbon footprint and often does little to resolve the original issue .
Data centre operators also need to recognise that optimising thermal performance positively impacts data centre risk management – however , it ’ s difficult to ask the right questions if you don ’ t actually have any granular visibility into how individual racks and cooling equipment are performing . Our research showed that only 5 % of data centre M & E teams currently monitor and report equipment temperature actively on an individual rack-by-rack basis – black art . You don ’ t need over-complex DCIM suites or expensive , non-realtime and often imprecise external CFD consultancy to tell you what ’ s going on across your own data centres . It ’ s much more useful to have an approach that gives you a real-time dynamic viewpoint of your mission-critical estate .
That ’ s why , for true data centre infrastructure management , M & E reporting tools need to get much more granular . The good news is that with the latest generation of software-driven data centre optimisation solutions , there ’ s a real opportunity to achieve this and – in turn – start to unlock the significant data centre carbon reduction that the wider organisation needs to deliver .
In contrast to traditional DCIM solutions that can take years to implement , software-driven thermal optimisation gives data centre teams much faster access to the insights they need for less cost and less human management overhead . At EkkoSense , we have found
Data centre operators also need to recognise that optimising thermal performance positively impacts data centre risk management .
and even less collect real-time cooling duty information or conduct any formal cooling resilience tests .
So how should data centre teams optimise their performance ?
While operators remain keen to secure carbon reductions , the reality for many is that they simply don ’ t have access to the tools that can help them to make smart data centre performance choices in real time . And , while legacy DCIM tools might be useful at helping operations teams manage their facilities , many find them limited when it comes to the deep data analysis needed to really optimise performance at the mechanical and electrical level .
So perhaps it ’ s time to stop treating efficient data centre operations as a that those organisations which deploy software-based optimisation solutions for critical infrastructure are able to reduce their cooling energy usage by up to 30 %, while at the same time releasing cooling capacity and unlocking immediate carbon savings .
Key innovations that have enabled this to happen include :
• The application of Machine Learning analytics built right into the heart of the solution , drawing not just on PhD-level thermal expertise but also data from 50m + data points in critical facilities around the world .
• Truly granular levels of sensing – taking advantage of the latest low-cost IoT wireless sensor technology to allow sensors to be deployed in higher numbers across the data centre right down
Dean Boyle , CEO , EkkoSense
to rack level – making true Machine Learning-based analytics and realtime thermal management of critical facilities a reality . This is typically complemented by vendor-agnostic cooling units that provide real-time cooling duty information .
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