Optimising space and energy
AI can also play a pivotal role in asset utilisation, one of the most complex tasks facing data centre managers. Equipment must be run at high performance levels to optimise power and floor space and minimise the energy wasted by running and cooling idle systems. Sensors can detect when servers are unused or underpowered, and AI automation can adjust virtualisation levels to reduce waste.
AI helps optimise electricity usage by analysing real-time power draw, temperature, and workload distribution data. Algorithms can then dynamically adjust cooling systems, power delivery, and server workloads to achieve peak efficiency. This reduces operational costs and extends equipment life since environmental factors are one of the leading causes of equipment failure.
Data centre capacity planning is a delicate balancing act that requires sufficient resources to meet current and future demands without overprovisioning. AI can analyse historical data, workload trends, and business forecasts to predict future resource requirements with precision humans can’t match. This avoids costly overspending on unused resources while ensuring enough capacity for future growth.
There are also indications that AI algorithms can achieve upwards of 90% accuracy in identifying components for recycling, while reducing health risks with robotic sorting. Given that less than 20% of e-waste is recycled today, this capability could make e-waste recycling faster and more affordable.
Despite all these benefits, integrating AI into existing on-premises data centre infrastructure is not without its asset management challenges. The need for substantial investments in AI technology and the skilled personnel to manage AI-driven systems are significant hurdles that enterprises must navigate if they choose to host their own AI workloads.
AI challenges
While most AI model training currently occurs in the cloud, AI applications are expected to trigger renewed investments in on-premises infrastructure. Reasons include data residency requirements, latency, the cost of transferring large data sets back and forth to cloud services and privacy/security regulations.
A recent Nutanix survey of IT decision-makers found that