109 citations · 222 across the 42 of their papers we have counts for
47 papers · 1 filter
Could Model Partitioning Make Federated Learning More Sustainable?
Tobias Frohlich, Tiffany Vlaar, Lauritz Thamsen
As federated learning (FL) extends from distributed machine learning between low-power devices to cross-silo scenarios involving edge servers and data centres, its carbon footprint…
How Accurately Can the Energy Use of Spark Applications Be Estimated Based on Resource Utilisation?
Youssef Moawad, Kathleen West, Vasilis Bountris +3
Distributed batch data processing applications are widely executed on cloud-based resources where restricted user access to node-level hardware energy counters hinders transparent…
Ichnos+: Estimating the Carbon Footprint of Scientific Workflows Using Fitted Power Models
Kathleen West, Youssef Moawad, Philipp Thamm +5
As data-intensive scientific workflows scale to facilitate the automation of analysis of increasing amounts of data, their resource-intensive and long-running execution incurs sign…
Predicting Lakehouse Performance in Clouds: An Empirical Exploration of Query Runtime Variance
James Nurdin, Wei Liu, Richard Mccreadie +1
Data analytics increasingly runs on distributed lakehouse systems, where platform operators must optimise monetary, resource, and environmental costs. Query Performance Prediction…
Augur: Pre-Execution Energy Prediction for Workflow Tasks in Heterogeneous Clusters
Kathleen West, Vasilis Bountris, Philipp Thamm +3
Scientific workflows are widely used to process large quantities of data, leading to significant energy consumption and carbon emissions. To reduce this environmental impact, energ…
Nf-PEAK: Process-Based Energy Attribution for Nextflow Workflows on Kubernetes Clusters
Philipp Thamm, Somayeh Mohammadi, Kathleen West +3
Scientific workflows are pipelines of interdependent tasks. They are increasingly executed on shared Kubernetes clusters via workflow engines such as Nextflow. Their energy consump…