This report provides a regional view of the results from the Uptime Institute Global Data Center Survey 2024 and highlights some of the different challenges and strategies of data center owners and operators across the globe.
Trust in AI as a tool for data center operations has declined sharply in the past three years. It is possible to control for the factors that drive mistrust — and see better outcomes when employees interact with AI-based systems.
Hydrogen is a promising energy storage medium that can help decarbonize infrastructure. It is not a great fit for the majority of data centers, and the hydrogen economy is not fully developed.
Most operators will be familiar with the outrageous power and cooling demands of hardware for generative AI. Why are these systems so difficult to accommodate, and what does this mean for the future of data center design?
Two-phase immersion was expected to revolutionize data center cooling but proved difficult to implement. With escalating silicon thermal power, two-phase is gaining substantial interest again, just in a different form: direct-to-chip liquid cooling.
This report discusses recent innovations in air cooling, such as advanced evaporative cooling methods, AI-driven facility management and cutting-edge server heat sinks.
Alongside continued developments in liquid cooling, there are reports of air-cooled data centers that achieve standout levels of efficiency. Uptime explores the factors that combine to enable exceptional efficiency in air cooling.
This report outlines the characteristics of machine learning (ML) applications, describes production use cases for ML-based software in data center M&O, and profiles several vendors offering AI-based functionality in their products.
Underwater data centers promise to be both economical and sustainable. The prerequisite densification of infrastructure and unmanned operations may only suit specific workloads, but lessons learned under water may influence land facilities.
This update examines the differences between machine learning and traditional software development and outlines the terms and definitions that may help digital infrastructure operators to understand the role and impact of AI.
Pressure to improve data center efficiency and sustainability is driving interest in artificial intelligence (AI) technologies. Several startups aim to deliver new capabilities in IT power management and cooling optimization.
A new approach to data center management, proposed by data scientists and statisticians, looks to augment the functionality of tools like BMS and DCIM software by focusing on data, not equipment.
Despite high expectations, most operators will only see moderate impact from specialized AI hardware installations in the immediate future. The emergence of AI as a major force will sway the industry in a more profound, but less direct, fashion.
Thermal trends in server silicon will challenge assumptions that underpin efficiency and sustainability expectations around DLC. Limited visibility of future server cooling requirements means operators can only make an educated guess.
The propensity to confidently give false information likely disqualifies generative AI from operational decision-making. However, this type of AI, with human supervision, could enhance other aspects of data center management.