Understanding Scientific Applications for Cloud Environments

Cloud Computing: Principles and Paradigms

Volumn , Issue , 2011, Pages 345-371

  Jha, Shantenu ^a,b   Katz, Daniel S ^c   Luckow, Andre ^a   Merzky, Andre ^a   Stamou, Katerina ^b  

^a LOUISIANA STATE UNIVERSITY   (United States)

^b Louisiana State University   (United States)

^c UNIVERSITY OF CHICAGO   (United States)

Author keywords

Classification of scientific applications and services in the cloud; SAGA based scientific applications that utilize clouds; Understanding scientific applications for cloud environments

Indexed keywords

EID: 84886180326     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9780470940105.ch13     Document Type: Chapter

References (54)

1. P. Mell and T. Grance, The NIST definition of cloud computing.
2. S. Jha et al., Programming Abstractions for Large-scale Distributed Applications, submitted to ACM Computing Surveys; draft at http://www.cct.lsu.edu/Bsjha/ publications/dpa_surveypaper.pdf.
3. L. Youseff, M. Butrico, and D. Da Silva, Toward a unified ontology of cloud computing, in Proceedings of the Grid Computing Environments Workshop, GCE '08, November 2008, 1-10.
4. M. Armbrust et al., Above the clouds: A Berkeley View of Cloud Computing, Technical Report UCB/EECS-2009-28, EECS Department, University of California, Berkeley, February 2009.
5. N. Wilkins-Diehr, D. Gannon, G. Klimeck, S. Oster, and S. Pamidighantam, TeraGrid science gateways andtheir impacton science. Computer, 41(11):32-41, 2008.
6. Google App Engine, http://code.google.com/appengine/.
7. Windows Azure, http://www.microsoft.com/windowsazure/.
8. P. Watson, D. Leahy, H. Hiden, S. Woodman, and J. Berry, An Azure Science Cloud for Drug Discovery, Microsoft External Research Symposium, 2009.
9. J. Dean and S. Ghemawat, MapReduce: Simplified data processing on large clusters, in Proceedings of the 6th Conference on Symposium on Operating Systems Design & Implementation, Berkeley, CA, USENIX Association, 2004, pp. 137-150.
10. Hadoop: Open Source Implementation of MapReduce, http://hadoop.apache.org/.
11. Amazon Elastic MapReduce, http://aws.amazon.com/elasticmapreduce/.
12. M. Isard, M. Budiu, Y. Yu, A. Birrell, and D. Fetterly, Dryad: Distributed dataparallel programs from sequential building blocks, SIGOPS Operating System Review, 41(3):59-72, 2007.
13. F. Chang et al., Bigtable: A distributed storage system for structured data, in Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation, pages 15-15, Berkeley, CA, USA, 2006. USENIX Association
14. Amazon EC2 Web Service, http://ec2.amazonaws.com.
15. Rackspace Cloud, http://www.rackspacecloud.com/.
16. GoGrid Cloud Hosting, http://www.gogrid.com/.
17. Eucalyptus, http://open.eucalyptus.com/.
18. K. Keahey, I. Foster, T. Freeman, and X. Zhang, Virtual workspaces: Achieving quality of service and quality of life in the grid, Scientific Programming, 13 (4):265-275, 2005.
19. Science Cloud. http://scienceclouds.org/.
20. Future Grid. http://www.futuregrid.org/.
21. Amazon S3 Web Service. http://s3.amazonaws.com.
22. Rackspace Cloud Files. http://www.rackspacecloud.com/cloud_hosting_ products/files/.
23. Eucalyptus Walrus. http://open.eucalyptus.com/wiki/EucalyptusStorage_v1.4.
24. S. Ghemawat, H. Gobioff, and S. Leung, The Google File System, SIGOPS Operating System Reviews, 37(5):29-43, 2003.
25. HDFS. http://hadoop.apache.org/common/docs/current/hdfs_design. html.
26. C. Evangelinos and C. Hill, Cloud computing for parallel scientific HPC applications: Feasibility of running coupled atmosphere-ocean climate models on Amazon's EC2, Cloud Computing and Its Applications (CCA-08), 2008.
27. M.-E. Bégin, Grids and Clouds-Evolution or Revolution, https://edms.cern.ch/file/925013/3/EGEE-Grid-Cloud.pdf, 2008.
28. A. Merzky, K. Stamou, and S. Jha, Application level interoperability between clouds and grids, in Proceedings of the Grid and Pervasive Computing Conference, May 2009, pp. 143-150.
29. T. Goodale et al., SAGA: A simple API for grid applications, high-level application programming on the grid, Computational Methods in Science and Technology, 12(1):7-20, 2006.
30. T. Goodale, S. Jha, H. Kaiser, T. Kielmann, P. Kleijer, A. Merzky, J. Shalf, and C. Smith, A Simple API for Grid Applications (SAGA), OGF Document Series 90, http://www.ogf.org/documents/GFD.90.pdf.
31. H. Kaiser, A. Merzky, S. Hirmer, and G. Allen, The SAGA C11 Reference Implementation, in Object-Oriented Programming, Systems, Languages and Applications (OOPSLA'06)-Library-Centric Software Design (LCSD'06), Portland, OR, USA, October 22-26 2006.
32. JSaga. http://grid.in2p3.fr/jsaga/index.html.
33. C. Miceli, M. Miceli, S. Jha, H. Kaiser, and A. Merzky, Programming abstractions for data intensive computing on clouds and grids, in Proceedings of the 9th IEEE/ ACM International Symposium on Cluster Computing and the Grid, May 2009, pp. 478-483.
34. CloudStore, http://kosmosfs.sourceforge.net.
35. HBase, http://hadoop.apache.org/hbase/.
36. The Montage project, http://montage.ipac.caltech.edu/.
37. G. B. Berriman, J. C. Good, D. Curkendall, J. Jacob, D. S. Katz, T. A. Prince, and R. Williams. Montage: An on-demand image mosaic service for the NVO, Astronomical Data Analysis Software and Systems (ADASS) XII, 2002.
38. A Merzky, K Stamou, S Jha, and D Katz, A fresh perspective on developing and executing DAG-based distributed applications: A case-study of SAGA-based Montage, in Proceedings of the IEEE Conference on eScience 2009, Oxford.
39. G. B. Berriman, J. C. Good, A. C. Laity, J. C. Jacob, D. S. Katz, E. Deelman, G. Singh, M.-H. Su, R. Williams, and T. Prince, Science applications of Montage: An astronomical image mosaic engine, presented at IAU XXXVI General Assembly, 2006.
40. E. Deelman et al., The cost of doing science on the cloud: The Montage example, Proceedings of SC08, Austin, Texas, 2008.
41. E. Deelman, J. Blythe, Y. Gil, C. Kesselman, G. Mehta, and K. Vahi, Mapping abstract complex workflows onto grid environments, Journal of Grid Computing, 1(1):25-39, 2003.
42. E. Deelman, J. Blythe, Y. Gil, C. Kesselman, G. Mehta, S. Patil, M.-H. Su, K. Vahi, and M. Livny, Pegasus: Mapping scientific workflows onto the grid, in Proceedings of the Across Grids Conference, 2004.
43. Condor DAGMAn, http://www.cs.wisc.edu/condor/dagman/.
44. M. Wieczorek, R. Prodan, and T. Fahringer, Scheduling of scientific workflows in the askalon grid environment, ACM SIGMOD Record, 34(3):52-62, 2005.
45. M. Humphrey and S. Park, Data throttling for data-intensive workflows, in Proceedings of the IEEE International Parallel and Distributed Processing Symposium, 1-11, 2008.
46. Y. Zhao et al., Swift: Fast, reliable, loosely coupled parallel computation, IEEE Congress on Services, pages 199-206, 2007.
47. H. Truong, T. Fahringer, and S. Dustda, Dynamic instrumentation, performance monitoring and analysis of grid scientific workflows, Journal of Grid Computing, 2005(3):1-18, 2005.
48. VGRaDS: Montage, a project providing a portable, compute-intensive service delivering custom mosaics on demand.
49. G. Juve, E. Deelman, K. Vahi, G. Mehta, B. Berriman, B. Berman, and P. Maechling, Scientific workflow applications on Amazon EC2, in Proceedings of the Workshop on Cloud-based Services and Applications in conjunction with 5th IEEE International Conference on e-Science (e-Science 2009), 2009.
50. H. Casanova, G. Obertelli, F. Berman, and R. Wolski, The apples parameter sweep template: User-level middleware for the grid, Scientific Programming, 8 (3):111-126, 2000.
51. A. Luckow, L. Lacinski, and S. Jha. Saga bigjob: An extensible and interoperable pilot-job abstraction for distributed applications and systems, in Proceedings of the 10th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, 2010.
52. E. Deelman, G. Singh, M. Livny, B. Berriman, and J. Good, The cost of doing science on the cloud: the montage example, in Proceedings of the 2008 ACM/IEEE Conference on Supercomputing, IEEE Press, New York, 2008, pp. 1-12.
53. SGI Cyclone-HPC Cloud, http://www.sgi.com/products/hpc_cloud/cyclone/index.html.
54. S. Jha, A. Merzky, and G. Fox, Using clouds to provide grids with higher levels of abstraction and explicit support for usage modes, Concurrency and Computation: Practice & Experience, 21(8):1087-1108, 2009.