THE DEBRIEF
are hyperconscious of the environmental footprint of data centres and their impact on local communities .
It will be interesting to see how the designation of data centres as Critical National Infrastructure ( CNI ) by the UK government will affect all stages of their lifecycle : site location , construction and operation . The main changes will involve increased government oversight , reporting and potential audits , along with new standards specific to data centres to ensure they meet high security and operational benchmarks .
TIM MITCHELL SALES DIRECTOR , KLIMA THERM
Data centres produce a lot of heat , which can very easily be captured , recycled and used in district heating systems . The barriers to widespread adoption of this approach are not technological . The market has all the machinery and skills required to use the heat created by data centres but there are legal and practical challenges – for out-of-town data centres in particular .
The legal issues of who is responsible for what elements of the system and the energy being fed and taken from it are initially significant , but certainly not insurmountable : where there ’ s a will there ’ s a way , and the balance of carrot ( e . g . financial incentives for participants ) and stick ( national or local planning rules ) must be found to drive uptake .
The practical challenges of what to do with the heat generated by these ‘ out-of-town ’ data centres can be solved by using the ‘ smart city ’ concept ; grouping net heat generators , like data centres , with industries or buildings that are net heat users , such as primary manufacturing facilities . For example , siting data centres near hospitals , hotels , leisure centres and housing developments provide a readymade , constant market for their heat . Again , the carrot / stick balance must be carefully managed to ensure a winwin situation for all participants .
AI is also continuing to be a challenging factor for day-to-day data centre design and architectures . For example , processing large AI workloads requires GPU servers to have significantly higher connectivity between them , but because of power and heat constraints , there is a limitation on the number of servers which can be installed in each rack . This leads to a situation where each GPU server connects to a switch within its row or room , requiring more inter-rack fibre cables than previously seen in cloud data centres , running 400G and 800G connections .
However , this is problematic . AI and Machine Learning ( ML ) algorithms are highly sensitive to latency – similar to High- Performance Computing – meaning AI clusters need to keep GPU servers located nearby , with most connections limited to 50 metres . That being said , not all data centres can accommodate GPU racks as a single cluster . These racks easily require over 40kW of power , forcing traditionally cooled data centres to spread them out , which wasn ’ t a problem in traditional data centres .
Cabling innovations allow data centres to navigate these narrow and congested GPU server-to-switch pathways and the increased cabling complexities that come with AI . Innovations like rollable ribbon fibre allow up to six 3,456 fibre cables to fit into a fourinch duct , doubling the density of traditional fibres , helping to keep GPU enabled Servers fed with the huge amounts of data they need to process Large Language Models ( LLMs ). Coupled together with new dense connector technologies like the MPO- 16 connector , network designs can provide both high-density connectivity and support of mainstream IEEE high-speed roadmap speeds to 1.6Tb . Essential for future-proofing networks in preparation for AI networks . �
The benefits of this approach are reciprocal . Moving heat to an ambient loop can make the data centre more efficient than if this heat were rejected to the atmosphere , as in an air-cooled chiller . This efficiency means less primary energy is required to run the data centre . The principle of one energy input and two useful energy outputs is a massive benefit to the overall carbon footprint of all buildings and infrastructure connected to the loop .
ALASTAIR WAITE SENIOR MANAGER , GLOBAL DATA CENTRE MARKET DEVELOPMENT , COMMSCOPE
It ’ s clear AI is affecting data centre construction , deployments and network architecture design in general . From a regulatory standpoint , power-hungry AI has been increasing the difficulty of securing approval for the building of new data centres ; regulators
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