109 citations · 463 across the 69 of their papers we have counts for
79 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…
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…
Proceedings of the 1st International Workshop on Low Carbon Computing (LOCO 2024)
Wim Vanderbauwhede, Lauritz Thamsen, José Cano
This is the proceedings of the 1st International Workshop on Low Carbon Computing (LOCO 2024).
Simultaneous Genetic Evolution of Neural Networks for Optimal SFC Embedding
Theviyanthan Krishnamohan, Lauritz Thamsen, Paul Harvey
The reliance of organisations on computer networks is enabled by network programmability, which is typically achieved through Service Function Chaining. These chains virtualise net…
Spatio-Temporal Shifting to Reduce Carbon, Water, and Land-Use Footprints of Cloud Workloads
Giulio Attenni, Youssef Moawad, Novella Bartolini +1
In this paper, we investigate the potential of spatial and temporal cloud workload shifting to reduce carbon, water, and land use footprints. Specifically, we perform a simulation…
HyProv: Hybrid Provenance Management for Scientific Workflows
Vasilis Bountris, Lauritz Thamsen, Ulf Leser
Provenance plays a crucial role in scientific workflow execution, for instance by providing data for failure analysis, real-time monitoring, or statistics on resource utilization f…
BENNS: A Surrogate Model for Hybrid Online-Offline Evolution of SFC Embedding
Theviyanthan Krishnamohan, Lauritz Thamsen, Paul Harvey
Service Function Chains (SFCs) enable programmatic control of the functions and services in a computer network. By leveraging Software Defined Networking to control the links betwe…
A Systematic Evaluation of the Potential of Carbon-Aware Execution for Scientific Workflows
Kathleen West, Youssef Moawad, Fabian Lehmann +4
Scientific workflows are critical to scientific data analysis and often involve computationally intensive processing of large datasets on compute clusters. As such, their execution…
Energy-Aware Workflow Execution: An Overview of Techniques for Saving Energy and Emissions in Scientific Compute Clusters
Lauritz Thamsen, Yehia Elkhatib, Paul Harvey +3
Scientific research in many fields routinely requires the analysis of large datasets, and scientists often employ workflow systems to leverage clusters of computers for their data…
Predicting the Performance of Scientific Workflow Tasks for Cluster Resource Management: An Overview of the State of the Art
Jonathan Bader, Kathleen West, Soeren Becker +5
Scientific workflow management systems support large-scale data analysis on cluster infrastructures. For this, they interact with resource managers which schedule workflow tasks on…
WOW: Workflow-Aware Data Movement and Task Scheduling for Dynamic Scientific Workflows
Fabian Lehmann, Jonathan Bader, Friedrich Tschirpke +6
Scientific workflows process extensive data sets over clusters of independent nodes, which requires a complex stack of infrastructure components, especially a resource manager (RM)…
Exploring the Potential of Carbon-Aware Execution for Scientific Workflows
Kathleen West, Fabian Lehmann, Vasilis Bountris +3
Scientific workflows are widely used to automate scientific data analysis and often involve processing large quantities of data on compute clusters. As such, their execution tends…
Flora: Efficient Cloud Resource Selection for Big Data Processing via Job Classification
Jonathan Will, Lauritz Thamsen, Jonathan Bader +1
Distributed dataflow systems like Spark and Flink enable data-parallel processing of large datasets on clusters of cloud resources. Yet, selecting appropriate computational resourc…
Experimentally Evaluating the Resource Efficiency of Big Data Autoscaling
Jonathan Will, Nico Treide, Lauritz Thamsen +1
Distributed dataflow systems like Spark and Flink enable data-parallel processing of large datasets on clusters. Yet, selecting appropriate computational resources for dataflow job…
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…