• • • DATACENTREMANAGEMENT • • •
constantly collected from the equipment connected, critical infrastructure monitoring can contribute to populating the data lake where the algorithms that are the basis of artificial intelligence work to enable it to evolve. Through algorithm evolution, the precision of the prediction related to data trending can continuously improve. High-density computing applications have two
main characteristics in terms of power consumption. First, they have a higher average energy demand due to processes such as model training and real-time inference, which generate significant electricity usage, even though the compute hardware used has reduced consumption per processing token. Secondly, the dynamic consumption profile varies depending on factors such as the type of tasks being executed, system configuration and graphics processing unit (GPU) architecture. To reduce financial, technical and safety risks
in the increasing complexity of modern data centres (especially with the demands of AI and High-Performance Computing) scalable and adaptable maintenance solutions are required providing the following: A maintenance approach that recognises potential issues before they occur by continuously measuring the health of critical infrastructure systems, identifying anomalies, alerting to health events and enabling lifecycle maintenance aligned with actual equipment needs. Advanced incident management support
offering troubleshooting, root cause analysis and
incident response by linking critical systems with expert engineers. Real-time data is monitored to spot trends, predict behaviours and address anomalies. Issues can be resolved remotely or by dispatching a field service engineer. This connected service optimises equipment performance and maximises availability. Customer portals offer an intuitive, cloud-based
interface for easy access to data centre asset information and graphical representations of the rapid or gradual declines in equipment health scores. With comprehensive dashboards, users can quickly make informed decisions, improving efficiency and reducing downtime risks. A crucial component of a digital world Critical infrastructure monitoring and management are indispensable in our digital era,
enabling the smooth operation of essential services. These systems, driven by advanced algorithms and artificial intelligence, actively identify and mitigate potential threats, contributing to the overall resilience of critical infrastructure. The integration of AI enhances predictive analytics, offering insights into equipment failure, maintenance needs, and environmental risks. Human-AI collaboration empowers decision- makers with deep insight that can easily be used to minimise errors and improve focus on strategic tasks, ultimately increasing operational resilience. These systems not only protect critical IT infrastructure but also evolve data lakes from simple trending to robust predictive tools. As our world becomes more digitally dependent, effective monitoring and management are crucial for providing the continuity and reliability of essential services. In summary, the role of critical infrastructure monitoring and management is extremely modern and forward looking. It makes use of critical equipment data to protect IT data, that is the servers stored within the racks, and therefore the continuity of the business and services relying on those servers. At the same time, by providing data to the data lake it can contribute to its evolution moving away from simple trending and heading towards more robust predictions about potential threats and anomalies that could affect critical infrastructure.
Surge PDevices
• • • Up to 100kA Imax • • 1 Phase + N & 3 Phase + N • • • • •
• • • • • Up to 50kA Imax • • 1 Phase + N & 3 Phase + N • • • •
t: 01785 818600 e:
sales@switchtec.com
www.switchtec.com
electricalengineeringmagazine.co.uk ELECTRICAL ENGINEERING • JUNE 2025 13
• • • Up to 40kA Imax • • • • • • • •
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