A carbon-aware Spark scheduler that defers low-priority tasks during high-carbon periods, using importance scores from an ML scheduler, reduced carbon by roughly a third in a 100-node prototype with near-neutral end-to-end completion time.
A learning-based scheduler for high volume processing in data warehouse using graph neural networks
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Carbon- and Precedence-Aware Scheduling for Data Processing Clusters
A carbon-aware Spark scheduler that defers low-priority tasks during high-carbon periods, using importance scores from an ML scheduler, reduced carbon by roughly a third in a 100-node prototype with near-neutral end-to-end completion time.