A Run-time System for Efficient Execution of Scientific Workflows on Distributed Environments

Scientific workflow systems have been introduced in response to the demand of researchers from several domains of science who need to process and analyze increasingly larger datasets. The design of these systems is largely based on the observation that data analysis applications can be composed as p...

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Published in:	International journal of parallel programming Vol. 36; no. 2; pp. 250 - 266
Main Authors:	Teodoro, George, Tavares, Tulio, Ferreira, Renato, Kurc, Tahsin, Meira, Wagner, Guedes, Dorgival, Pan, Tony, Saltz, Joel
Format:	Journal Article
Language:	English
Published:	Boston Springer US 01.04.2008 Springer Nature B.V
Subjects:	Computer Science Data analysis Data processing Datasets Experiments Parallel processing Processor Architectures Software Software Engineering/Programming and Operating Systems Studies Theory of Computation Parallel computing Data-analysis Scientific workflows
ISSN:	0885-7458, 1573-7640
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Abstract	Scientific workflow systems have been introduced in response to the demand of researchers from several domains of science who need to process and analyze increasingly larger datasets. The design of these systems is largely based on the observation that data analysis applications can be composed as pipelines or networks of computations on data. In this work, we present a run-time support system that is designed to facilitate this type of computation in distributed computing environments. Our system is optimized for data-intensive workflows, in which efficient management and retrieval of data, coordination of data processing and data movement, and check-pointing of intermediate results are critical and challenging issues. Experimental evaluation of our system shows that linear speedups can be achieved for sophisticated applications, which are implemented as a network of multiple data processing components.
AbstractList	Scientific workflow systems have been introduced in response to the demand of researchers from several domains of science who need to process and analyze increasingly larger datasets. The design of these systems is largely based on the observation that data analysis applications can be composed as pipelines or networks of computations on data. In this work, we present a runtime support system that is designed to facilitate this type of computation in distributed computing environments. Our system is optimized for data-intensive workflows, in which efficient management and retrieval of data, coordination of data processing and data movement, and check-pointing of intermediate results are critical and challenging issues. Experimental evaluation of our system shows that linear speedups can be achieved for sophisticated applications, which are implemented as a network of multiple data processing components.Scientific workflow systems have been introduced in response to the demand of researchers from several domains of science who need to process and analyze increasingly larger datasets. The design of these systems is largely based on the observation that data analysis applications can be composed as pipelines or networks of computations on data. In this work, we present a runtime support system that is designed to facilitate this type of computation in distributed computing environments. Our system is optimized for data-intensive workflows, in which efficient management and retrieval of data, coordination of data processing and data movement, and check-pointing of intermediate results are critical and challenging issues. Experimental evaluation of our system shows that linear speedups can be achieved for sophisticated applications, which are implemented as a network of multiple data processing components. Scientific workflow systems have been introduced in response to the demand of researchers from several domains of science who need to process and analyze increasingly larger datasets. The design of these systems is largely based on the observation that data analysis applications can be composed as pipelines or networks of computations on data. In this work, we present a run-time support system that is designed to facilitate this type of computation in distributed computing environments. Our system is optimized for data-intensive workflows, in which efficient management and retrieval of data, coordination of data processing and data movement, and check-pointing of intermediate results are critical and challenging issues. Experimental evaluation of our system shows that linear speedups can be achieved for sophisticated applications, which are implemented as a network of multiple data processing components. Scientific workflow systems have been introduced in response to the demand of researchers from several domains of science who need to process and analyze increasingly larger datasets. The design of these systems is largely based on the observation that data analysis applications can be composed as pipelines or networks of computations on data. In this work, we present a runtime support system that is designed to facilitate this type of computation in distributed computing environments. Our system is optimized for data-intensive workflows, in which efficient management and retrieval of data, coordination of data processing and data movement, and check-pointing of intermediate results are critical and challenging issues. Experimental evaluation of our system shows that linear speedups can be achieved for sophisticated applications, which are implemented as a network of multiple data processing components. Scientific workflow systems have been introduced in response to the demand of researchers from several domains of science who need to process and analyze increasingly larger datasets. The design of these systems is largely based on the observation that data analysis applications can be composed as pipelines or networks of computations on data. In this work, we present a run-time support system that is designed to facilitate this type of computation in distributed computing environments. Our system is optimized for data-intensive workflows, in which efficient management and retrieval of data, coordination of data processing and data movement, and check-pointing of intermediate results are critical and challenging issues. Experimental evaluation of our system shows that linear speedups can be achieved for sophisticated applications, which are implemented as a network of multiple data processing components. [PUBLICATION ABSTRACT]
Author	Saltz, Joel Tavares, Tulio Guedes, Dorgival Meira, Wagner Pan, Tony Ferreira, Renato Kurc, Tahsin Teodoro, George
AuthorAffiliation	Department of Computer Science, Universidade Federal de Minas Gerais, 31270-010 Belo Horizonte, MG - Brazil, tel +553499-5860 - fax +553499-5858 Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210 - USA, tel +1292-4778 - fax +1688-6600
AuthorAffiliation_xml	– name: Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210 - USA, tel +1292-4778 - fax +1688-6600 – name: Department of Computer Science, Universidade Federal de Minas Gerais, 31270-010 Belo Horizonte, MG - Brazil, tel +553499-5860 - fax +553499-5858
Author_xml	– sequence: 1 givenname: George surname: Teodoro fullname: Teodoro, George email: george@dcc.ufmg.br organization: Department of Computer Science, Universidade Federal de Minas Gerais – sequence: 2 givenname: Tulio surname: Tavares fullname: Tavares, Tulio organization: Department of Computer Science, Universidade Federal de Minas Gerais – sequence: 3 givenname: Renato surname: Ferreira fullname: Ferreira, Renato organization: Department of Computer Science, Universidade Federal de Minas Gerais – sequence: 4 givenname: Tahsin surname: Kurc fullname: Kurc, Tahsin organization: Department of Biomedical Informatics, The Ohio State University – sequence: 5 givenname: Wagner surname: Meira fullname: Meira, Wagner organization: Department of Computer Science, Universidade Federal de Minas Gerais – sequence: 6 givenname: Dorgival surname: Guedes fullname: Guedes, Dorgival organization: Department of Computer Science, Universidade Federal de Minas Gerais – sequence: 7 givenname: Tony surname: Pan fullname: Pan, Tony organization: Department of Biomedical Informatics, The Ohio State University – sequence: 8 givenname: Joel surname: Saltz fullname: Saltz, Joel organization: Department of Biomedical Informatics, The Ohio State University
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References	Acharya, A., Uysal, M., Saltz, J.: Active disks: Programming model, algorithms and evaluation. In: Eighth International Conference on Architectural Support for Programming Languages and Operations Systems (ASPLOS VIII), pp. 81–91 (1998) Hastings, S., Langella, S., Oster, S., Saltz, J.: Distributed data management and integration framework: The mobius project. In: Global Grid Forum 11 (GGF11) Semantic Grid Applications Workshop, pp. 20–38. IEEE Computer Society (2004) Frey, J., Tannenbaum, T., Foster, I., Livny, M., Tuecke, S. Condor-G: A computation management agent for multi-institutional grids. In: Proceedings of the Tenth IEEE Symposium on High Performance Distributed Computing (HPDC10). IEEE Press (2001) Lee, E.A., Parks, T.M.: Dataflow process networks. In: Proceedings of the IEEE, pp. 773–799 (1995) Casanova, H., Dongarra, J.: Netsolve: A network enabled server for solving computational science problems. In: International Journal of Supercomputer, pp. 212–223 (1997) Kola, G., Kosar, T., Frey, J., Livny, M., Brunner, R.J., Remijan, M.: Disc: a system for distributed data intensive scientific computing. In: Proceeding of the First Workshop on Real, Large Distributed Systems (WORLDS’04). San Francisco, CA (2004) Foster, I., Voeckler, J., Wilde, M., Zhao, Y.: Chimera: A virtual data system for representing, querying, and automating data derivation. In: The 14th International Conference on Scientific and Statistical Database Management (SSDBM’02) (2002) Berglund, A., Boag, S., Chamberlim, D., Fernández, M.F., Kay, M., Robie, J., Siméon, J.: Xml path language (xpath). In: World Wide Web Consortium (W3C) (2003) Altintas, I., Berkley, C., Jaeger, E., Jones, M., Ludscher, B., Mock, S.: Kepler: An extensible system for design and execution of scientific workflows. In: The 16th International Conference on Scientific and Statistical Database Management(SSDBM). Santorini Island, Greece (2004) Pan, T.C., Huang, K.: Virtual mouse placenta: Tissue layer segmentation. In: Proceedings of the 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC2005) (2005) CERN: Large hadron collider (lhc)-http://www.interactions.org/lhc PTOLEMYII project, Department of EECS, US Berkeley http://ptolemy.eecs.berkeley.edu/ptolemyII/ (2004) Hastings, S., Ribeiro, M., Langella, S., Oster, S., Catalyurek, U., Pan, T., Huang, K., Ferreira, R., Saltz, J., Kurc, T.: Xml database support for distributed execution of data-intensive scientific workflows. SIGMOD Record 34, 2005 Deelman, E., Blythe, J., Gil, Y., Kesselman, C., Mehta, G., Vahi, K., Lazzarini, A., Arbree, A., Cavanaugh, R., Koranda, S.: Mapping abstract complex workflows onto grid environments. J. Grid Comput. 25–39 (2003) Ferreira, R., Meira, W. Jr., Guedes, D., Drummond, L., Coutinho, B., Teodoro, G., Tavares, T., Araujo, R., Ferreira, G.: Anthill: A scalable run-time environment for data mining applications. In: Symposium on Computer Architecture and High-Performance Computing (SBAC-PAD) (2005) Tatebe, O., Morita, Y., Matsuoka, S., Soda, N., Sekiguchi, S.: Grid datafarm architecture for petascale data intensive computing. In: 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGrid) (2002) Ludascher, B., Altintas, I., Berkley, C., Higgins, D., Jaeger-Frank, E., Jones, M., Lee, E., Tao, J., Zhao, Y.: Scientific workflow management and the kepler system. Concurrency and Computation: Practice & Experience, Special Issue on Scientific Workflows (2005) 68_CR3 68_CR2 68_CR1 68_CR17 68_CR9 68_CR15 68_CR8 68_CR16 68_CR7 68_CR6 68_CR5 68_CR4 68_CR10 68_CR13 68_CR14 68_CR11 68_CR12
References_xml	– reference: Ludascher, B., Altintas, I., Berkley, C., Higgins, D., Jaeger-Frank, E., Jones, M., Lee, E., Tao, J., Zhao, Y.: Scientific workflow management and the kepler system. Concurrency and Computation: Practice & Experience, Special Issue on Scientific Workflows (2005) – reference: Acharya, A., Uysal, M., Saltz, J.: Active disks: Programming model, algorithms and evaluation. In: Eighth International Conference on Architectural Support for Programming Languages and Operations Systems (ASPLOS VIII), pp. 81–91 (1998) – reference: Foster, I., Voeckler, J., Wilde, M., Zhao, Y.: Chimera: A virtual data system for representing, querying, and automating data derivation. In: The 14th International Conference on Scientific and Statistical Database Management (SSDBM’02) (2002) – reference: Hastings, S., Langella, S., Oster, S., Saltz, J.: Distributed data management and integration framework: The mobius project. In: Global Grid Forum 11 (GGF11) Semantic Grid Applications Workshop, pp. 20–38. IEEE Computer Society (2004) – reference: Deelman, E., Blythe, J., Gil, Y., Kesselman, C., Mehta, G., Vahi, K., Lazzarini, A., Arbree, A., Cavanaugh, R., Koranda, S.: Mapping abstract complex workflows onto grid environments. J. Grid Comput. 25–39 (2003) – reference: Altintas, I., Berkley, C., Jaeger, E., Jones, M., Ludscher, B., Mock, S.: Kepler: An extensible system for design and execution of scientific workflows. In: The 16th International Conference on Scientific and Statistical Database Management(SSDBM). Santorini Island, Greece (2004) – reference: Ferreira, R., Meira, W. Jr., Guedes, D., Drummond, L., Coutinho, B., Teodoro, G., Tavares, T., Araujo, R., Ferreira, G.: Anthill: A scalable run-time environment for data mining applications. In: Symposium on Computer Architecture and High-Performance Computing (SBAC-PAD) (2005) – reference: Hastings, S., Ribeiro, M., Langella, S., Oster, S., Catalyurek, U., Pan, T., Huang, K., Ferreira, R., Saltz, J., Kurc, T.: Xml database support for distributed execution of data-intensive scientific workflows. SIGMOD Record 34, 2005 – reference: Lee, E.A., Parks, T.M.: Dataflow process networks. In: Proceedings of the IEEE, pp. 773–799 (1995) – reference: CERN: Large hadron collider (lhc)-http://www.interactions.org/lhc/ – reference: Kola, G., Kosar, T., Frey, J., Livny, M., Brunner, R.J., Remijan, M.: Disc: a system for distributed data intensive scientific computing. In: Proceeding of the First Workshop on Real, Large Distributed Systems (WORLDS’04). San Francisco, CA (2004) – reference: Berglund, A., Boag, S., Chamberlim, D., Fernández, M.F., Kay, M., Robie, J., Siméon, J.: Xml path language (xpath). In: World Wide Web Consortium (W3C) (2003) – reference: PTOLEMYII project, Department of EECS, US Berkeley http://ptolemy.eecs.berkeley.edu/ptolemyII/ (2004) – reference: Casanova, H., Dongarra, J.: Netsolve: A network enabled server for solving computational science problems. In: International Journal of Supercomputer, pp. 212–223 (1997) – reference: Tatebe, O., Morita, Y., Matsuoka, S., Soda, N., Sekiguchi, S.: Grid datafarm architecture for petascale data intensive computing. In: 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGrid) (2002) – reference: Frey, J., Tannenbaum, T., Foster, I., Livny, M., Tuecke, S. Condor-G: A computation management agent for multi-institutional grids. In: Proceedings of the Tenth IEEE Symposium on High Performance Distributed Computing (HPDC10). IEEE Press (2001) – reference: Pan, T.C., Huang, K.: Virtual mouse placenta: Tissue layer segmentation. In: Proceedings of the 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC2005) (2005) – ident: 68_CR1 – ident: 68_CR9 doi: 10.1145/291069.291026 – ident: 68_CR16 – ident: 68_CR14 – ident: 68_CR15 doi: 10.1177/109434209701100304 – ident: 68_CR13 – ident: 68_CR17 – ident: 68_CR12 doi: 10.1007/978-3-540-28642-4_2 – ident: 68_CR10 – ident: 68_CR11 – ident: 68_CR3 – ident: 68_CR8 – ident: 68_CR2 – ident: 68_CR5 – ident: 68_CR7 doi: 10.1109/5.381846 – ident: 68_CR4 doi: 10.1145/1084805.1084815 – ident: 68_CR6 doi: 10.1002/cpe.994
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SubjectTerms	Computer Science Data analysis Data processing Datasets Experiments Parallel processing Processor Architectures Software Software Engineering/Programming and Operating Systems Studies Theory of Computation
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Title	A Run-time System for Efficient Execution of Scientific Workflows on Distributed Environments
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Volume	36
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