DATA CENTRE MANAGEMENT


Responding to the AI challenge


The rapid rise of artificial intelligence (AI) is redefining the role of data centres, accelerating energy demand and placing data centres under intense scrutiny. How can we best respond to the challenge and do so sustainably? asks Andrew Donoghue.


Andrew Donoghue


Global segment strategy at Vertiv


www.vertiv.com F


or data centres to support the AI growth, while aligning with global sustainability goals, innovative strategies


to manage energy use and minimise environmental impact will be needed. AI applications, particularly those


involving deep learning and large- scale data processing, require significant computational power. This translates into an increase in energy consumption. According to the International Energy Agency (IEA) Data centres are already responsible for around 1% of global electricity use, and it is expected that their demands will grow exponentially as AI adoption increases. Training a single large AI model could consume as much energy as 100+ households use in a year. Traditional data centres designed


for less intensive workloads often struggle to manage this surge in demand efficiently. Cooling systems are pushed to their limits, power distribution becomes more complex, and overall energy efficiency can be impacted. As a result, operators are obviously looking for ways to redesign their infrastructure to better support the unique needs of AI while keeping energy consumption and costs under control.


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Rethinking cooling Liquid cooling is emerging as a key technology in this space, offering a more effective way to manage the high heat densities associated with AI. By cooling components with liquid, these systems can maintain optimal temperatures even under extreme loads.


Liquid cooling systems can also be


integrated with heat reuse strategies, where the excess heat generated by AI workloads is captured and repurposed for other uses, such as heating buildings or supporting industrial processes. This approach not only improves energy efficiency but also contributes to the overall sustainability of the data centre.


Alternative energy The push for sustainability in data centres goes beyond improving energy efficiency – it also involves reducing reliance on non-renewable energy sources. As AI drives up power consumption, integrating alternative energy becomes increasingly critical. Solar, wind, and hydropower are all viable options for data centres looking to reduce their carbon footprint and support sustainable growth. Data centres are increasingly


exploring ways to incorporate alternative energy sources into their operations. On-site solar installations, for example, can contribute to a data centre’s energy needs, particularly in warmer climates. Similarly, wind power can be integrated into data centre energy strategies, either through


on-site turbines or by partnering with local wind farms. However, the intermittent nature


of renewable energy sources poses a challenge. AI workloads require consistent, high levels of power, which can be difficult to achieve with solar or wind alone. To address this, data centres are investing in energy storage solutions, such as battery systems, that can store excess energy generated during peak production periods and release it when needed.


Optimising operations AI is not only a driver of increased energy use – it can also be part of the solution. Data centres are beginning to use AI and machine learning to optimise their own operations, leveraging these technologies to improve energy efficiency and reduce waste. By analysing patterns in energy use and planning for future demand, AI can help data centres dynamically allocate resources, adjust cooling strategies, and manage power distribution in real-time. For example, AI algorithms can suggest when a data centre will


AI is not only a driver of increased energy use – it can also be part of the solution


experience peak demand and pre- emptively adjust cooling systems to allow optimal performance. Similarly, AI can be used to monitor the health of equipment, avoiding failures before they occur and reducing the risk of energy-wasting outages. Additionally, AI can play a role in managing the integration of alternative energy. By looking at weather patterns and energy production from solar or wind sources, AI can help data centres plan their energy use more effectively, enabling them to make the most of available renewable resources while minimising reliance on non-renewable backups.


Scalable designs As AI continues to evolve, so too must the data centres that support it. It is crucial that data centre infrastructure can meet the demands of AI today but also be ready for sustainability in the long term. This means designing facilities that can scale with AI’s growing computational needs without a proportional increase in energy consumption. One approach is to adopt modular


data centre designs that can be expanded as needed without major overhauls. Prefabricated modular units can be deployed quickly and configured to meet specific requirements, providing a flexible, scalable solution that can grow alongside AI applications. These units are often more energy-efficient than traditional designs, and are purpose- built for high-density environments. Another important consideration is the development of data centres that can support a circular economy. This involves designing systems and choosing materials that can be easily repurposed or recycled at the end of their lifecycle, reducing waste and environmental impact.


Collaborative effort The transformation of data centres to support AI sustainably is not a challenge that operators can tackle alone. It requires collaboration across the entire technology ecosystem, including IT hardware manufacturers, software developers, energy providers, and regulatory bodies. By working together, these stakeholders can drive innovation and develop standards that support both technological advancement and environmental responsibility. Ultimately, the rise of AI presents both an opportunity and a challenge for data centres. While the computational demands of AI are driving up energy use, they also offer a chance to innovate and lead the way toward a more sustainable future. ■


EIBI | NOVEMBER � DECEMBER 2024


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