2023 – THE YEAR TO DEPLOY MACHINE LEARNING AND AI IN YOUR DATA CENTER ?
cCan you use Machine Learning and AI to balance the impossible – increased data center workloads v energy reduction / ESG targets ?
Today ’ s data centers face a challenge that , initially , looks like it ’ s almost impossible to resolve . Operations have never been busier , and analyst projections suggest workload levels are only going to increase over the next five years – with some projecting compound annual growth rates of as much as 21 % through 2025 . At the same time , critical facilities such as data centers are coming under increased pressure to reduce energy consumption – particularly as the reality of corporate ESG programs and net zero commitments start to bite .
So how can data centers resolve what appear to be two potentially conflicting demands : supporting escalating workloads while still cutting their carbon usage and helping to support ESG goals ?
In this White Paper EkkoSense argues that traditional data center software toolsets – such as BMS , EPMS ,
CFD and DCIM – can ’ t provide a credible answer as they don ’ t equip operations teams with a complete view of what ’ s really happening in their data centers .
Instead , the answer requires a fundamentally different and more innovative approach , combining more comprehensive sensing , advanced software optimization tools and the power of Machine Learning and AI algorithms to provide a true real-time visualization of data center performance .
This White Paper sets out how Machine Learning and an AI-enabled software-based optimization can now play a key role in helping data centers to deliver against both workload demands and energy reduction goals . p