UNCOVERING THE LAYERS
THIS EXCESSIVE COOLING REMAINS A HUGE CONSUMER OF ENERGY – REPRESENTING AROUND 35 % OF A DATA CENTRE ’ S OVERALL ENERGY CONSUMPTION AND PROVING A BARRIER TO CARBON REDUCTION INITIATIVES . for human auditability . You make the suggested changes and then use the software tool to confirm that the changes are delivering the expected results .
In adopting this approach , we ’ re recognising that data centres never stay the same , so this kind of ongoing optimisation allows teams to keep up with their ever-changing environment . Additionally , by deploying cooling duty sensors to track cooling loads , we ’ re also now able to identify previously undetected cooling unit faults before they are even picked up by BMS alerts . By capturing entirely new levels of data centre cooling data – going beyond basic temperature measurements to also include energy usage and airflow distribution – you can map zone-by-zone cooling analytics within the data centre to
help with resiliency and capacity planning decisions . This creates live cooling ‘ zones of influence ’ that can group racks into clusters along with the cooling unit that is providing the cooling . Additional information can also be overlaid such as floor vent airflows .
So , while it ’ s certainly possible to automate many of these processes and feedback performance updates into the Machine Learning loop , I question whether most data centres are ready to take this leap . That ’ s why we remain focused on providing a software-based optimisation approach that delivers intelligent decision support based auditable , actionable cooling , power and space recommendations – rather than simply automating what is always going to be an evolving process . ◊
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