Hewlett Packard Enterprise (HPE) is working with the US Department of Energy’s National Renewable Energy Laboratory (NREL) to develop artificial intelligence (AI) and machine learning (ML) technologies to automate and improve operational efficiency, including resiliency and energy usage, in data centres.
The AI Ops R&D collaboration is part of a three-year partnership that introduces monitoring and predictive analytics to power and cooling systems in NREL’s Energy Systems Integration Facility (ESIF) HPC Data Center.
HPE and NREL are using over five years’ worth of historical data, totalling more than 16 terabytes of data, collected from sensors in NREL’s supercomputers and its facility, to train models for anomaly detection with the aim of predicting and preventing issues before they occur.
According to HPE, early results based on models trained with historical data have successfully predicted or identified events that previously occurred in NREL’s data centre, demonstrating the potential value of using predictive analytics in this way.
The collaboration will also address future water and energy consumption in data centres, with the partners aiming to optimise energy efficiency and sustainability as measured by key metrics such as power usage effectiveness (PUE), water usage effectiveness (WUE) and carbon usage effectiveness (CUE).
Applying AI-driven operations to an exascale supercomputer – running at a speed that represents a thousandfold increase over today’s systems – will enable energy-efficient operations, and increase resiliency and reliability through smart and automated capabilities, HPE said.
The research collaboration will span the areas of data management, data analytics, and AI/ML optimisation for both manual and autonomous intervention in data centre operations, explained Kristin Munch, manager of NREL’s Data, Analysis and Visualization Group.
“We’re excited to join HPE in this multi-year, multi-staged effort – and we hope to eventually build capabilities for an advanced smart facility after demonstrating these techniques in our existing data centre,” Munch added.
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