aceA carbon aware job scheduling framework for data center sustainability using deep learning training
Abstract Deep learning workloads have experienced rapid growth which has resulted in higher energy consumption and increased carbon emissions for contemporary data centres. The existing solutions of carbon tracking and static scheduling systems provide insufficient capacity to implement carbon awareness in actual machine learning operational processes. In this paper, we present EcoSchedAI (Eco-...
Rapid growth of deep learning workloads increases energy consumption and carbon emissions, while current carbon tracking and static scheduling fail to deliver carbon awareness in real ML operations.
Evidence
- Peer-reviewedDiscover Artificial Intelligence2026-08-16
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Truvace Impact Record TRV-2026-0812, v1: “A carbon aware job scheduling framework for data center sustainability using deep learning training.” Truvace, 2026-08-17. /record/TRV-2026-0812 (accessed at citation time). sha256 64219a727daea446…
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