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Carbon Responder: Coordinating Demand Response for the Datacenter Fleet

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arxiv 2311.08589 v1 pith:DNHZB7WT submitted 2023-11-14 cs.DC cs.AR

classification cs.DCcs.AR
keywords carbonresponderworkloadschallengesdatacentersfairfootprintframework
verification ladder T0 review T1 audit T2 compute T3 formal

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The increasing integration of renewable energy sources results in fluctuations in carbon intensity throughout the day. To mitigate their carbon footprint, datacenters can implement demand response (DR) by adjusting their load based on grid signals. However, this presents challenges for private datacenters with diverse workloads and services. One of the key challenges is efficiently and fairly allocating power curtailment across different workloads. In response to these challenges, we propose the Carbon Responder framework. The Carbon Responder framework aims to reduce the carbon footprint of heterogeneous workloads in datacenters by modulating their power usage. Unlike previous studies, Carbon Responder considers both online and batch workloads with different service level objectives and develops accurate performance models to achieve performance-aware power allocation. The framework supports three alternative policies: Efficient DR, Fair and Centralized DR, and Fair and Decentralized DR. We evaluate Carbon Responder polices using production workload traces from a private hyperscale datacenter. Our experimental results demonstrate that the efficient Carbon Responder policy reduces the carbon footprint by around 2x as much compared to baseline approaches adapted from existing methods. The fair Carbon Responder policies distribute the performance penalties and carbon reduction responsibility fairly among workloads.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Coordinating GPU Data Centers and Power Grid Regulation Service for Exogenous Carbon Benefits

    cs.DC 2026-01 unverdicted novelty 6.0 of 10

    GPU data centers that track grid frequency-regulation signals can avoid enough fossil-fuel reserve emissions to outweigh the operational carbon of the workloads they modulate, at low-to-medium utilization.

  2. Turning AI Data Centers into Grid-Interactive Assets: Results from a Field Demonstration in Phoenix, Arizona

    cs.DC 2025-07 conditional novelty 5.0 of 10

    A software-only orchestration platform reduced power use of a production 256-GPU AI cluster by 25% for 3 hours during utility peak events while holding jobs within agreed performance limits.

  3. Environmental Burden of United States Data Centers in the Artificial Intelligence Era

    cs.CY 2024-11 conditional novelty 5.0 of 10

    US data centers consumed 192.64 TWh and emitted 105.59 million tons of CO2e in the past year, with a carbon intensity 48% above the US average.

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