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- research-articleApril 2024
End-to-end Integration of Scientific Workflows on Distributed Cyberinfrastructures: Challenges and Lessons Learned with an Earth Science Application
- Camila Roa,
- Mats Rynge,
- Paula Olaya,
- Karan Vahi,
- Todd Miller,
- James Griffioen,
- Shelley Knuth,
- John Goodhue,
- David Hudak,
- Alana Romanella,
- Ricardo Llamas,
- Rodrigo Vargas,
- Miron Livny,
- Ewa Deelman,
- Michela Taufer
UCC '23: Proceedings of the IEEE/ACM 16th International Conference on Utility and Cloud ComputingArticle No.: 17, Pages 1–9https://doi.org/10.1145/3603166.3632142Distributed cyberinfrastructures (CI) pose opportunities and challenges for the execution of scientific workflows, especially in the context of Earth science applications. They provide heterogeneous resources that can meet the needs of the applications ...
- research-articleJanuary 2016
Pegasus in the Cloud: Science Automation through Workflow Technologies
IEEE Internet Computing (IEEECS_INTERNET), Volume 20, Issue 1Pages 70–76https://doi.org/10.1109/MIC.2016.15The Pegasus Workflow Management System maps abstract, resource-independent workflow descriptions onto distributed computing resources. As a result of this planning process, Pegasus workflows are portable across different infrastructures, optimizable for ...
- ArticleOctober 2014
Community Resources for Enabling Research in Distributed Scientific Workflows
E-SCIENCE '14: Proceedings of the 2014 IEEE 10th International Conference on e-Science - Volume 01Pages 177–184https://doi.org/10.1109/eScience.2014.44A significant amount of recent research in scientific workflows aims to develop new techniques, algorithms and systems that can overcome the challenges of efficient and robust execution of ever larger workflows on increasingly complex distributed ...
- articleSeptember 2013
A Case Study into Using Common Real-Time Workflow Monitoring Infrastructure for Scientific Workflows
- Karan Vahi,
- Ian Harvey,
- Taghrid Samak,
- Daniel Gunter,
- Kieran Evans,
- David Rogers,
- Ian Taylor,
- Monte Goode,
- Fabio Silva,
- Eddie Al-Shakarchi,
- Gaurang Mehta,
- Ewa Deelman,
- Andrew Jones
Journal of Grid Computing (SPJGC), Volume 11, Issue 3Pages 381–406https://doi.org/10.1007/s10723-013-9265-4Scientific workflow systems support various workflow representations, operational modes, and configurations. Regardless of the system used, end users have common needs: to track the status of their workflows in real time, be notified of execution ...
- articleMarch 2013
Characterizing and profiling scientific workflows
Future Generation Computer Systems (FGCS), Volume 29, Issue 3Pages 682–692https://doi.org/10.1016/j.future.2012.08.015Researchers working on the planning, scheduling, and execution of scientific workflows need access to a wide variety of scientific workflows to evaluate the performance of their implementations. This paper provides a characterization of workflows from ...
- ArticleSeptember 2011
Online Fault and Anomaly Detection for Large-Scale Scientific Workflows
HPCC '11: Proceedings of the 2011 IEEE International Conference on High Performance Computing and CommunicationsPages 373–381https://doi.org/10.1109/HPCC.2011.55Scientific workflows are an enabler of complex scientific analyses. Large-scale scientific workflows are executed on complex parallel and distributed resources, where many things can fail. Application scientists need to track the status of their ...
- articleSeptember 2010
Scaling up workflow-based applications
- Scott Callaghan,
- Ewa Deelman,
- Dan Gunter,
- Gideon Juve,
- Philip Maechling,
- Christopher Brooks,
- Karan Vahi,
- Kevin Milner,
- Robert Graves,
- Edward Field,
- David Okaya,
- Thomas Jordan
Journal of Computer and System Sciences (JCSS), Volume 76, Issue 6Pages 428–446https://doi.org/10.1016/j.jcss.2009.11.005Scientific applications, often expressed as workflows are making use of large-scale national cyberinfrastructure to explore the behavior of systems, search for phenomena in large-scale data, and to conduct many other scientific endeavors. As the ...
- articleSeptember 2010
Parameterized specification, configuration and execution of data-intensive scientific workflows
- Vijay S. Kumar,
- Tahsin Kurc,
- Varun Ratnakar,
- Jihie Kim,
- Gaurang Mehta,
- Karan Vahi,
- Yoonju Lee Nelson,
- P. Sadayappan,
- Ewa Deelman,
- Yolanda Gil,
- Mary Hall,
- Joel Saltz
Cluster Computing (KLU-CLUS), Volume 13, Issue 3Pages 315–333https://doi.org/10.1007/s10586-010-0133-8Data analysis processes in scientific applications can be expressed as coarse-grain workflows of complex data processing operations with data flow dependencies between them. Performance optimization of these workflows can be viewed as a search for a set ...
- research-articleMay 2008
Provenance: The Bridge Between Experiments and Data
Computing in Science and Engineering (IEEECS_CISE-NEW), Volume 10, Issue 3Pages 38–46https://doi.org/10.1109/MCSE.2008.82Current scientific applications are often structured as workflows and rely on workflow systems to compile abstract experiment designs into enactable workflows that utilize the best available resources. The automation of this step, and of the workflow ...
- ArticleDecember 2007
Connecting Scientific Data to Scientific Experiments with Provenance
E-SCIENCE '07: Proceedings of the Third IEEE International Conference on e-Science and Grid ComputingPages 179–186https://doi.org/10.1109/E-SCIENCE.2007.22As scientific workflows and the data they operate on, grow in size and complexity, the task of defining how those workflows should execute (which resources to use, where the resources must be in readiness for processing etc.) becomes proportionally more ...
- articleDecember 2007
Optimizing workflow data footprint
- Gurmeet Singh,
- Karan Vahi,
- Arun Ramakrishnan,
- Gaurang Mehta,
- Ewa Deelman,
- Henan Zhao,
- Rizos Sakellariou,
- Kent Blackburn,
- Duncan Brown,
- Stephen Fairhurst,
- David Meyers,
- G. Bruce Berriman,
- John Good,
- Daniel S. Katz
In this paper we examine the issue of optimizing disk usage and scheduling large-scale scientific workflows onto distributed resources where the workflows are data-intensive, requiring large amounts of data storage, and the resources have limited storage ...
- ArticleMay 2007
Scheduling Data-IntensiveWorkflows onto Storage-Constrained Distributed Resources
- Arun Ramakrishnan,
- Gurmeet Singh,
- Henan Zhao,
- Ewa Deelman,
- Rizos Sakellariou,
- Karan Vahi,
- Kent Blackburn,
- David Meyers,
- Michael Samidi
CCGRID '07: Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the GridPages 401–409https://doi.org/10.1109/CCGRID.2007.101In this paper we examine the issue of optimizing disk usage and of scheduling large-scale scientific workflows onto distributed resources where the workflows are dataintensive, requiring large amounts of data storage, and where the resources have ...
- ArticleJune 2003
The role of planning in Grid computing
Grid computing gives users access to widely distributed networks of computing resources to solve large-scale tasks such as scientific computation. These tasks are defined as standalone components that can be combined to process the data in various ways. ...