Carbon-Native DCIM Architectures for AI Data Centers: Autonomous Infrastructure Control via Smart Grid Intelligence
Regional System Architect, Schneider Electric, USA.
Research Article
World Journal of Advanced Research and Reviews, 2024, 21(01), 3008-3318
Publication history:
Received on 5 December 2023; revised on 21January 2024; accepted on 28 January 2024
Abstract:
Data center infrastructure management (DCIM) has traditionally prioritized operational metrics such as power usage effectiveness, availability, and cost optimization while treating carbon emissions as a secondary reporting metric. This paradigm is fundamentally misaligned with the urgent need for decarbonization in computing infrastructure, which currently accounts for approximately two percent of global electricity consumption. This paper introduces a novel carbon-intelligent DCIM framework that elevates real-time grid carbon intensity to a first-class control variable, enabling autonomous optimization of data center operations toward carbon-negative targets. Unlike conventional approaches that react to energy pricing signals or static sustainability reports, the proposed system integrates predictive carbon intensity forecasting, temporal workload orchestration, and adaptive infrastructure control into a unified decision engine. The framework employs machine learning models trained on multi-day grid carbon intensity patterns, weather correlations, and facility-specific thermal characteristics to anticipate low-carbon operational windows. Dynamic control loops modulate cooling system configurations, battery energy storage discharge schedules, and compute workload placements to align power consumption with periods of minimal grid carbon intensity. Validation through simulation across hyperscale compute scenarios demonstrates carbon emission reductions of thirty-two percent while maintaining strict service level agreements for mission-critical workloads. The system achieves carbon-negative operation during renewable energy abundance periods by strategically timing compute-intensive operations and thermal storage utilization. This research establishes foundational principles for embedding decarbonization objectives directly into infrastructure control systems, transforming DCIM from a passive monitoring platform into an active participant in grid decarbonization strategies. The framework addresses critical gaps in autonomous sustainability management for AI training facilities, federal compute infrastructure, and energy-intensive manufacturing environments.
Keywords:
Carbon-Aware Computing; Data Center Infrastructure Management; Grid Decarbonization; Marginal Carbon Intensity; Autonomous Sustainability Optimization; Renewable Energy Integration; Temporal Load Balancing
Full text article in PDF:
Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0