Catalogue of Tools & Metrics for Trustworthy AI

These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.

ITU-T L.1305 - Data centre infrastructure management system based on big data and artificial intelligence technology



This Recommendation describes specifications of a data centre infrastructure management (DCIM) system based on big data and artificial intelligence (AI) technology. The system will manage all infrastructure in the data centre at the same time through a comprehensive platform. 
The scope of this Recommendation includes:

  • network infrastructure of management systems;
  • standardization of data collection of the installed module;
  • interconnection among various kinds of monitoring sub-systems;
  • requirements for different functions in a DCIM. 

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The information about this standard has been compiled by the AI Standards Hub, an initiative dedicated to knowledge sharing, capacity building, research, and international collaboration in the field of AI standards. You can find more information and interactive community features related to this standard by visiting the Hub’s AI standards database here. To access the standard directly, please visit the developing organisation’s website.

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Tags:

  • System architecture
  • Data collection
  • Data processing
  • Interoperability
  • data sharing

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Disclaimer: The tools and metrics featured herein are solely those of the originating authors and are not vetted or endorsed by the OECD or its member countries. The Organisation cannot be held responsible for possible issues resulting from the posting of links to third parties' tools and metrics on this catalogue. More on the methodology can be found at https://oecd.ai/catalogue/faq.