Top 10: Uses of AI in Data Centres

The integration of AI in data centres is revolutionising how facilities operate, from providing unprecedented efficiency to maximising security and transforming scalability. AI technologies are stepping in as a vital solution for data centre operators, in streamlining operations and transforming various aspects of data centre management. AI's role in data centres is having an enormous impactful, with the potential still growing. 

Here are our Top 10 uses for AI in data centres - a list which will certainly evolve.

10. Training & development

According to research by digital infrastructure company Equinix, 62% of global IT decision-makers view a shortage of personnel with IT skills as one of the main threats to their business.

New AI technologies are among the most widely-cited solutions to the current talent crisis. Pioneering talent specialists across the world are utilising AI to help clients achieve a more efficient, supportive and intuitive approach to both onboarding and talent retention. 

“In terms of supporting talent, AI can be very effective as a tool supporting augmented reality training scenarios, providing efficient real-time operational analytics and for attracting talent by demonstrating that a business is leveraging new and emerging technologies to provide employees with more interesting and future proof roles,” Mick Lane, the Global Technology Solutions Manager at CBRE.

9. Infrastructure management

In our Top 10 5G Infrastructure Companies we reviewed the leading companies in 5G infrastructure,with a commitment to providing better connectivity for all. Qualcomm has been developing 5G consistently for years. The company’s 5G Advanced services will support new devices, services, spectrum and deployments. It also views 5G and AI as complementary advancing together and mutually benefiting each other in terms of performance and efficiency.

8. Data processing & storage

AI supports data processing and storage for data centres in many ways. AI can continuously monitor system parameters and adjust them to optimise processing performance, which ensures that processing tasks are executed efficiently.

Data can be automatically moved between different storage tiers (such as SSDs, HDDs and cloud storage) based on usage patterns. This ensures that data accessed most frequently is stored on faster, more expensive media, while infrequently accessed data is moved to cheaper, slower media. AI can also analyse and identify redundant data and suggest compression or deduplication techniques to save storage space.

7. Smart cooling 

AI and high-performance computing have put increased demand on data centre power. As a result, data centres have had to find ways to keep themselves cool, which has led to the development of smart cooled solutions. To paraphrase Plato, necessity is the mother of invention. 

Smart cooling uses AI and machine learning to adjust cooling parameters, unlike traditional air and liquid cooling which rely on predefined settings and manual adjustments.

Smart cooling optimises energy use by predicting conditions and responding to them in real-time. Smart cooling can also adapt to changes in server workload and environmental conditions automatically, while traditional systems are less adaptable.

6. Power management 

AI supports power management in data centres through a variety of ways. AI algorithms can allocate computing tasks based on power availability, while also making sure that power usage is balanced across the data centre. AI can also predict power usage and move non-critical tasks to off-peak times, in order to avoid excessive energy consumption and reduce costs. Data on energy consumption patterns can be collected and analysed by AI, providing insights into how power is being used and where efficiencies can be gained.

Energy performance across different parts of the data centre can be compared, identifying areas for improvement and helping set benchmarks for energy efficiency.

5. Predictive maintenance 

Predictive maintenance refers to a more preemptive approach to data centre operation by utilising AI technology to predict what needs repairing. According to Deloitte, predictive maintenance is able to increase enterprise productivity by 25%, reduce breakdowns by 70% and lower maintenance costs by 25% - as opposed to reactive maintenance.

“AI needs an enormous amount of data to learn and evolve, so as data centres grow, more data will be available for AI to use,” said Flora Cavinato, Global Service Product Portfolio Director at Vertiv. “Predictive maintenance will naturally scale, which means that the more dense and varied data population there is, the better the data trending, pattern recognition, insight learning and predictions.”

4. Network automation 

AI supports network automation in data centres by enhancing efficiency, reliability and performance in a range of ways. AI can automatically configure network settings based on real-time traffic, which ensures optimal network performance and reduces manual intervention. AI-driven systems can support network policies across the data centre, updating configurations automatically to comply with security policies.

AI also allows for automatic scaling of network resources in response as workloads change. This makes sure that the network can handle different levels of traffic, without manual intervention.

3. Resource optimisation 

Hyperscale data centres are built by companies with vast data processing and storage needs. These firms may derive their income directly from the applications or websites the equipment supports, or sell technology management services to third parties. As they are significantly larger than enterprise data centres, they are an attractive choice for data centre operators. 

Many big hyperscalers are exploring new technologies, such as AI, to further improve their data centre efficiency. For example, Meta is exploring how AI and machine learning can help it to optimise its data centre operations, especially surrounding the construction of a new data centre campus in Singapore, which will be one of the largest in the region.

2. Security 

Security is a big factor in every industry, from factory floor to the CEO’s office. The use of AI in data centres strengthens security in several ways: 

• AI can monitor network traffic and user behaviour to spot anomalies which may suggest a security threat. 
• AI-powered intrusion detection and prevention systems can identify threats effectively, by adapting to new data of emerging threats.
• Following a security incident, AI can automate a response, reducing the time taken to contain threats. This can help to isolate affected systems and block malicious IP addresses.
• Within the data centre infrastructure, AI can help to identify vulnerabilities. Machine learning models can suggest which vulnerabilities are most likely to be targeted allowing for immediate action.
• Through the automation of security tasks, AI minimises the need for manual intervention, lowers operational costs and allows security teams to put their energy into strategic activities.

1. Sustainability 

Data centres which support AI are faced with the challenge of higher computational power, prompting greater changes in facility design, newer cooling technologies and greater innovation in carbon footprint reduction.

As the data centre sector is called upon to confront its sustainability challenges, Data Centre Magazine spoke with Tom Kingham, Design Lead Europe & Japan at CyrusOne. He explained how data centres are starting to evolve into renewable energy developers to confront the energy efficiency challenge, in addition to how facilities are looking to be more flexible - both digitally and environmentally.

According to Tom, data centres should continue to address the unique demands of AI workloads, including the need for ultra-high-density power usage, advanced cooling technologies and scalable designs.

“This has been an ongoing priority for CyrusOne, guiding the design of current and future projects to effectively scale their AI infrastructure,” he said. “Inevitably, we are seeing a stronger focus on sustainability, with innovations reducing physical space requirements and carbon footprints, reflecting a broader trend towards greener data centre operations.”

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