Pegasus: A framework for mapping complex scientific workflows onto distributed systems. This paper describes the Pegasus framework that can be used to map complex scientific workflows onto distributed resources. Pegasus enables users to represent the workflows at an abstract level without needing to worry about the particulars of the target execution systems. The paper describes general issues in mapping applications and the functionality of Pegasus. We present the results of improving application performance through workflow restructuring which clusters multiple tasks in a workflow into single entities. A real-life astronomy application is used as the basis for the study

References in zbMATH (referenced in 31 articles )

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  1. Pacini, Elina; Mateos, Cristian; García Garino, Carlos: Multi-objective swarm intelligence schedulers for online scientific clouds (2016)
  2. Tsamoura, Efthymia; Gounaris, Anastasios; Manolopoulos, Yannis: Optimization of decentralized multi-way join queries over pipelined filtering services (2012)
  3. Byun, Eun-Kyu; Kee, Yang-Suk; Kim, Jin-Soo; Deelman, Ewa; Maeng, Seungryoul: BTS: Resource capacity estimate for time-targeted science workflows (2011)
  4. Han, Liangxiu; Liew, Chee Sun; van Hemert, Jano; Atkinson, Malcolm: A generic parallel processing model for facilitating data mining and integration (2011)
  5. Mika, Marek; Waligóra, Grzegorz; Wȩglarz, Jan: Modelling and solving grid resource allocation problem with network resources for workflow applications (2011)
  6. Wang, Chenqi; Cafferkey, Neil; Kennedy, James; Morrison, John P.: CG3DR: coordination of icosahedral virus reconstruction using condensed graphs (2011)
  7. Wilde, Michael; Hategan, Mihael; Wozniak, Justin M.; Clifford, Ben; Katz, Daniel S.; Foster, Ian: Swift: A language for distributed parallel scripting (2011)
  8. Wu, Qishi; Gu, Yi: Modeling and simulation of distributed computing workflows in heterogeneous network environments (2011)
  9. Yuan, Dong; Yang, Yun; Liu, Xiao; Chen, Jinjun: On-demand minimum cost benchmarking for intermediate dataset storage in scientific cloud workflow systems (2011)
  10. Bittencourt, Luiz Fernando; Madeira, Edmundo R.M.: Towards the scheduling of multiple workflows on computational grids (2010)
  11. Callaghan, Scott; Deelman, Ewa; Gunter, Dan; Juve, Gideon; Maechling, Philip; Brooks, Christopher; Vahi, Karan; Milner, Kevin; Graves, Robert; Field, Edward; Okaya, David; Jordan, Thomas: Scaling up workflow-based applications (2010)
  12. Camarasu-Pop, Sorina; Glatard, Tristan; Mościcki, Jakub T.; Benoit-Cattin, Hugues; Sarrut, David: Dynamic partitioning of GATE Monte-Carlo simulations on EGEE (2010)
  13. Kumar, Vijay S.; Kurc, Tahsin; Ratnakar, Varun; Kim, Jihie; Mehta, Gaurang; Vahi, Karan; Nelson, Yoonju Lee; Sadayappan, P.; Deelman, Ewa; Gil, Yolanda: Parameterized specification, configuration and execution of data-intensive scientific workflows (2010)
  14. Raicu, Ioan; Foster, Ian; Wilde, Mike; Zhang, Zhao; Iskra, Kamil; Beckman, Peter; Zhao, Yong; Szalay, Alex; Choudhary, Alok; Little, Philip: Middleware support for many-task computing (2010)
  15. Yang, Chao-Tung; Leu, Fang-Yie; Chen, Sung-Yi: Network Bandwidth-aware job scheduling with dynamic information model for Grid resource brokers (2010)
  16. Yu, Li; Moretti, Christopher; Thrasher, Andrew; Emrich, Scott; Judd, Kenneth; Al.: Harnessing parallelism in multicore clusters with the all-pairs, wavefront, and makeflow abstractions (2010)
  17. Zinn, Daniel; Bowers, Shawn; Köhler, Sven; Ludäscher, Bertram: Parallelizing XML data-streaming workflows via MapReduce (2010)
  18. Barker, Adam; Weissman, Jon B.; van Hemert, Jano I.: The Circulate architecture: avoiding workflow bottlenecks caused by centralised orchestration (2009)
  19. Bui, Hoang; Kelly, Michael; Lyon, Christopher; Pasquier, Mark; Thomas, Deborah; Al.: Experience with BXGrid: a data repository and computing grid for biometrics research (2009)
  20. Yang, Chao-Tung; Lai, Kuan-Chou; Shih, Po-Chi: Design and implementation of a workflow-based resource broker with information system on computational grids (2009)

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