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Artificial Intelligence is emerging quickly as a key tool in the operation and management of datacentres . As datacentres continue to become more business-critical , more technologically complex and networked , and more reliant on empirical / datadriven rather than ‘ hunch ’ decision making so human capabilities are proving increasing challenged in managing the datacentre environment . The complexity of management increases further as datacentres become absorbed into portfolios of digital infrastructure comprised of different storage processing and distribution environments . The complexity of management increases further as datacentres become absorbed into portfolios of digital infrastructure comprised of different storage , processing and distribution environments . Almost 20 % of 600 datacentre owners and operators in a 2019 DCD survey indicate that they are currently deploying AI in their datacentres and a similar proportion have this as a future priority .
Cooling is very much a target for AI deployments . In most datacentres , cooling and heat removal is the single largest consumer of energy after the IT equipment and it is a major source of risk as too little cooling may lead to thermal events and the shut-down of IT equipment . This source of risk means that excessive or poorly targeted cooling represents a major source of waste since cooling represents an average of 40 % of energy costs in a datacentre and a 2017 DCD study in South East Asia indicated that power costs could be halved if PUE was improved from 1.80 ( close to the global average ) down to 1.30 . While a seismic improvement of this scale is unlikely , even more moderate improvements will make a difference to operational costs . ◊
14 Issue 22 www . intelligentdatacentres . com