Modern artificial intelligence (AI) and machine learning (ML) workloads create unprecedented challenges for data center infrastructure coordination. AI training applications can generate power fluctuations exceeding 200 MW within 40 ms intervals, creating grid-destabilizing events equivalent to a quarter-million people suddenly appearing on the electrical grid.
Current data center infrastructure lacks consistent, coordinated mechanisms for moving critical operational data between workload management systems, power infrastructure, and cooling systems. This absence of standardized data interfaces creates information silos that prevent effective coordination and optimization across the complete workload-to-infrastructure pathway.
This Technical Specification establishes the Workload Dynamic Power and Cooling (WDPC) framework for standardized data movement and coordination between computational workloads and infrastructure systems. WDPC addresses the fundamental challenge of creating consistent, temporal data standards that enable intelligent coordination without prescribing specific control implementations.
The framework establishes three primary objectives: standardization of temporal data formats and metadata structures for power and cooling systems; creation of consistent instrumentation and monitoring interfaces across workload-to-infrastructure pathways; and enablement of coordinated optimization through standardized data availability rather than centralized control.
WDPC provides the foundational data infrastructure necessary for innovation in workload-infrastructure coordination while maintaining flexibility for diverse implementation approaches across traditional organizational boundaries.
This Technical Specification establishes the Workload Dynamic Power and Cooling (WDPC) framework for standardized data coordination between computational workloads and energy infrastructure systems. WDPC addresses the critical need for consistent temporal data standards in environments where artificial intelligence (AI) and machine learning (ML) applications generate power fluctuations exceeding 200 MW within temporal intervals of 40 ms.
<aside> 📅 This project meets every 2 weeks on a Monday at 16:30 BST as part of the Hardware Standards WG
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