• • • DATA CENTRE MANAGEMENT • • •
The crucial role of infrastructure management
in the AI era By Jon Abbott, Technologies Director, Global Strategic Clients at Vertiv
ata centre operations managers traditionally depend on events and alarms from various management systems such as BMS (battery management systems) and EPMS (electrical power management systems).
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This operational data supports daily activities but lacks real-time, product-specific information for detailed analytics. As a result, energy inefficiencies can be missed, increasing costs and hindering sustainability. Crucial real-time data, like fluid flow and pressure rates, is limited and advanced predictive analytics for equipment maintenance are absent. Today, organisations are investing in critical digital infrastructure to support the numerous business productivity advantages presented by data and artificial intelligence (AI) applications. Constant monitoring and efficient management of IT infrastructure is paramount to enable uninterrupted operations and prevent potential disruptions that could have far-reaching consequences for businesses and end-users alike. High density computing for AI applications, where operation depends heavily on infrastructure performance, requires power supply and cooling systems to adapt to specific consumption and cooling demand profiles.
This means that traditional maintenance and operational practices need to be reviewed and supplemented with specialist analytical and site support. The importance of monitoring and managing critical infrastructure cannot be overstated, and the use of enhanced data analytics for infrastructure management is essential to provide an effective solution.
Monitoring and threat identification
It is the continuous exchange of data with the critical equipment and the adoption of a monitoring system that allows the discovery of potential threats that could impact business or service continuity. The identification of patterns and anomalies in the collection of large amounts of data permits a faster and more accurate problem detection, diagnosis and resolution. This monitoring of critical equipment adds an important layer of protection to continuity, and therefore availability of the infrastructure. By leveraging sophisticated algorithms, some monitoring systems can predict equipment failure and maintenance needs based on data analysis. Analysing historical performance data and real- time parametric data provided by critical equipment makes it possible to forecast when infrastructure elements like power and cooling equipment could potentially fail, allowing for predictive maintenance to prevent costly breakdowns and long restoration time. Monitoring and management systems can also help to optimise the utilisation of critical equipment by operating it more efficiently. For example, identifying stranded capacity thus reducing energy waste and costs. This is made possible by analysing the vast amounts of data coming from sensors, equipment and other sources and presenting them to operators and decision makers in a more understandable and actionable format. It can also contribute to reducing human errors, by automating many decisions-making processes.
Combining monitoring with remote control capabilities makes it possible to reduce the need for on-site personnel and to enhance the ability to manage the infrastructure in challenging or remote sites and locations.
Addressing environmental
factors Factors such as heat, humidity and moisture can significantly impact the performance of data centre equipment. Integrating environmental sensors into the monitoring and management system enables the identification of potential risks, allowing for proactive measures to minimise their impact. For instance, real-time monitoring of temperature fluctuations can prevent overheating, while humidity sensors can detect and mitigate moisture-related issues, safeguarding sensitive equipment.
Modern infrastructure management goes beyond immediate risk mitigation with efficiency increasingly becoming a priority. Incorporating environmental considerations into infrastructure management involves optimising energy usage and reducing carbon footprints. Monitoring systems can play a pivotal role in this by identifying opportunities to enhance energy efficiency, tackle resource wastage and contribute to the overall efficiency of operations. Through monitoring leveraging and management systems, organisations can analyse power consumption patterns and explore opportunities to integrate alternative energy sources. This enhances operational resilience by diversifying the energy mix using a variety of energy sources instead of relying on a single source like fossil fuels, this could be wind, solar or hydro, and reducing dependence on conventional power grids.
The AI factor
The integration of AI takes critical infrastructure monitoring to new heights. The ability to use the data collected by monitoring systems to identify trends and predict outcomes is a relatively new feature that has been added to the traditional monitoring of connected equipment to detect potential anomalies and be notified about it. This capability is one of the most interesting advances in critical infrastructure monitoring and management. Thanks to the emergence of AI, identifying trends and making predictions can be taken even further and adds more intelligence to monitoring and management solutions. By its very nature, AI needs an enormous amount of data to learn and evolve. If data is
12 ELECTRICAL ENGINEERING • JUNE 2025
electricalengineeringmagazine.co.uk
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