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University of Glasgow
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External monitoring can drastically improve the accuracy of energy estimates for Spark applications, reducing significant underestimations.
Ichnos+ achieves a groundbreaking 10.8% estimation error in carbon footprint calculations, outperforming existing methodologies and expanding the scope of environmental impact assessments in scientific workflows.
Query runtimes in lakehouses can vary by nearly 100%, but addressing this variance can boost prediction accuracy by up to 80% and reduce carbon costs significantly.
Pinpointing the energy footprint of individual tasks within scientific workflows running on Kubernetes is now more accurate, even amidst noisy, co-located workloads.
