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

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  1. Agrawal, Tarun K.; Sahu, Aryabartta; Ghose, Manojit; Sharma, R.: Scheduling chained multiprocessor tasks onto large multiprocessor system (2017)
  2. Pacini, Elina; Mateos, Cristian; García Garino, Carlos: Multi-objective swarm intelligence schedulers for online scientific clouds (2016) ioport
  3. Ma, Yinglong; Shi, Moyi; Wei, Jun: Cost and accuracy aware scientific workflow retrieval based on distance measure (2015)
  4. Stehr, Mark-Oliver; Kim, Minyoung; McCarthy, Tim: A distributed computing model for dataflow, controlflow, and workflow in fractionated cyber-physical systems (2014)
  5. Tsamoura, Efthymia; Gounaris, Anastasios; Manolopoulos, Yannis: Optimization of decentralized multi-way join queries over pipelined filtering services (2012)
  6. Byun, Eun-Kyu; Kee, Yang-Suk; Kim, Jin-Soo; Deelman, Ewa; Maeng, Seungryoul: BTS: Resource capacity estimate for time-targeted science workflows (2011) ioport
  7. Han, Liangxiu; Liew, Chee Sun; van Hemert, Jano; Atkinson, Malcolm: A generic parallel processing model for facilitating data mining and integration (2011) ioport
  8. Mika, Marek; Waligóra, Grzegorz; Węglarz, Jan: Modelling and solving grid resource allocation problem with network resources for workflow applications (2011)
  9. Wang, Chenqi; Cafferkey, Neil; Kennedy, James; Morrison, John P.: CG3DR: coordination of icosahedral virus reconstruction using condensed graphs (2011) ioport
  10. Wilde, Michael; Hategan, Mihael; Wozniak, Justin M.; Clifford, Ben; Katz, Daniel S.; Foster, Ian: Swift: A language for distributed parallel scripting (2011) ioport
  11. Wu, Qishi; Gu, Yi: Modeling and simulation of distributed computing workflows in heterogeneous network environments (2011) ioport
  12. Yuan, Dong; Yang, Yun; Liu, Xiao; Chen, Jinjun: On-demand minimum cost benchmarking for intermediate dataset storage in scientific cloud workflow systems (2011)
  13. Bittencourt, Luiz Fernando; Madeira, Edmundo R.M.: Towards the scheduling of multiple workflows on computational grids (2010) ioport
  14. 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) ioport
  15. Camarasu-Pop, Sorina; Glatard, Tristan; Mościcki, Jakub T.; Benoit-Cattin, Hugues; Sarrut, David: Dynamic partitioning of GATE Monte-Carlo simulations on EGEE (2010) ioport
  16. Joseph C. Jacob, Daniel S. Katz, G. Bruce Berriman, John Good, Anastasia C. Laity, Ewa Deelman, Carl Kesselman, Gurmeet Singh, Mei-Hui Su, Thomas A. Prince, Roy Williams: Montage: a grid portal and software toolkit for science-grade astronomical image mosaicking (2010) arXiv
  17. 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) ioport
  18. 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) ioport
  19. Yang, Chao-Tung; Leu, Fang-Yie; Chen, Sung-Yi: Network Bandwidth-aware job scheduling with dynamic information model for Grid resource brokers (2010) ioport
  20. 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) ioport

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