Artificial Intelligence for Data Center Operations (AIOps)

Caleb Phillips, Austin Todd, Avi Purkayastha, Hilary Egan, David Sickinger, Matthew Eash, Sergey Serebryakov, Jeff Hanson, Matt Slaby, Nick Wunder, Nalinrat Guba, Kristin Munch, Tahir Cader

Research output: NRELTechnical Report


HPC data centers such as the one at NREL's ESIF will increasingly need to rely on automation to keep pace with exascale growth in compute capability and to manage and optimize the data center environment and facility resources. Artificial intelligence (AI) and machine learning (ML) approaches provide the means to improve HPC data center efficiency (energy, operational, and managerial efficiency) and resiliency by learning historical trends and training models to operate on real-time data collected from both IT and facilities sources. The goal of coupled improvement of data center resiliency and energy efficiency through automated data collection and AI has led to a multi-year, multi-staged collaboration between NREL and Hewlett-Packard Enterprise's Advanced Technology Group, referred to as Artificial Intelligence for Data Center Operations (AIOps). The extended efforts within the AIOps project include a common goal of building capabilities for an advanced smart facility and demonstration of data collection and AI modeling techniques in the ESIF data center.
Original languageAmerican English
Number of pages20
StatePublished - 2021

NREL Publication Number

  • NREL/TP-2C00-79712


  • AI
  • data center
  • energy efficiency
  • machine learning
  • operations
  • supercomputer


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