21 papers
Scoring and Gamification to Encourage Sustainable Use of Compute Clusters
Maximilian MacDonald, Chris McCaig, Sean MacAvaney +2
The environmental cost of computing continues to grow, yet behaviour change remains limited. We present a composite sustainability score integrating average carbon intensity, resou…
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…