109 citations · 463 across the 69 of their papers we have counts for
8 papers · 1 filter
Ichnos: A Carbon Footprint Estimator for Scientific Workflows
Kathleen West, Magnus Reid, Yehia Elkhatib +1
Scientific workflows facilitate the automation of data analysis, and are used to process increasing amounts of data. Therefore, they tend to be resource-intensive and long-running,…
Workflows Community Summit 2024: Future Trends and Challenges in Scientific Workflows
Rafael Ferreira da Silva, Deborah Bard, Kyle Chard +108
The Workflows Community Summit gathered 111 participants from 18 countries to discuss emerging trends and challenges in scientific workflows, focusing on six key areas: time-sensit…
KS+: Predicting Workflow Task Memory Usage Over Time
Jonathan Bader, Ansgar Lößer, Lauritz Thamsen +2
Scientific workflow management systems enable the reproducible execution of data analysis pipelines on cluster infrastructures managed by resource managers such as Kubernetes, Slur…
Ponder: Online Prediction of Task Memory Requirements for Scientific Workflows
Fabian Lehmann, Jonathan Bader, Ninon De Mecquenem +5
Scientific workflows are used to analyze large amounts of data. These workflows comprise numerous tasks, many of which are executed repeatedly, running the same custom program on d…
Sizey: Memory-Efficient Execution of Scientific Workflow Tasks
Jonathan Bader, Fabian Skalski, Fabian Lehmann +4
As the amount of available data continues to grow in fields as diverse as bioinformatics, physics, and remote sensing, the importance of scientific workflows in the design and impl…
Scaling on Frontier: Uncertainty Quantification Workflow Applications using ExaWorks to Enable Full System Utilization
Mikhail Titov, Robert Carson, Matthew Rolchigo +6
When running at scale, modern scientific workflows require middleware to handle allocated resources, distribute computing payloads and guarantee a resilient execution. While indivi…
Privacy-Preserving Sharing of Data Analytics Runtime Metrics for Performance Modeling
Jonathan Will, Dominik Scheinert, Jan Bode +3
Performance modeling for large-scale data analytics workloads can improve the efficiency of cluster resource allocations and job scheduling. However, the performance of these workl…
Demeter: Resource-Efficient Distributed Stream Processing under Dynamic Loads with Multi-Configuration Optimization
Morgan Geldenhuys, Dominik Scheinert, Odej Kao +1
Distributed Stream Processing (DSP) focuses on the near real-time processing of large streams of unbounded data. To increase processing capacities, DSP systems are able to dynamica…