Autonomic management of application workflows on hybrid computing infrastructure

Scientific Programming

Volumn 19, Issue 2-3, 2011, Pages 75-89

  Kim, Hyunjoo ^a   El Khamra, Yaakoub ^b,c   Rodero, Ivan ^a   Jha, Shantenu ^a,c,d   Parashar, Manish ^a  

^a RUTGERS UNIVERSITY   (United States)

^b UNIVERSITY OF TEXAS AT AUSTIN   (United States)

^c LOUISIANA STATE UNIVERSITY   (United States)

^d Louisiana State University   (United States)

Author keywords

Cloud computing; distributed computing; hybrid computing

Indexed keywords

CLOUDS; COMPUTER SOFTWARE SELECTION AND EVALUATION; COMPUTER SYSTEMS; CRITICAL INFRASTRUCTURES; MANAGEMENT; PETROLEUM RESERVOIR ENGINEERING; PETROLEUM RESERVOIRS;

AMAZON EC2; APPLICATION MANAGEMENT; APPLICATION REQUIREMENTS; AUTONOMIC MANAGEMENT; AUTONOMIC SYSTEMS; BUDGET CONSTRAINT; COMMODITY CLUSTERS; COMPLEX APPLICATIONS; COMPUTING INFRASTRUCTURES; HIGH PERFORMANCE COMPUTING; HYBRID COMPUTING; OIL RESERVOIRS; RUNTIMES; TERAGRID; WORK-FLOWS;

CLOUD COMPUTING;

EID: 79960794179     PISSN: 10589244     EISSN: None     Source Type: Journal    
DOI: 10.3233/SPR-2011-0319     Document Type: Article

References (34)

1. S. Balay, K. Buschelman,W.D. Gropp, D. Kaushik, M.G. Knepley, L.C. McInnes, B.F. Smith and H. Zhang, PETSc Web page, 2001, available at: Http://www.mcs.anl.gov/petsc.
2. N. Bobroff, L. Fong, S. Kalayci, Y. Liu, J.C. Martinez, I. Rodero, S.M. Sadjadi and D. Villegas, Enabling interoperability among meta-schedulers, in: CCGRID'08: Proceedings of the 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid, IEEE Computer Society, Washington, DC, USA, 2008, pp. 306-315.
3. R. Buyya, C.S. Yeo, S. Venugopal, J. Broberg and I. Brandic, Cloud computing and emerging it platforms: Vision, hype, and reality for delivering computing as the 5th utility, Future Gener. Comput. Syst. 25 (2009), 599-616.
4. N. Carriero and D. Gelernter, Linda in context, Commun. ACM 32(4) (1989), 444-458.
5. L. Chunlin and L. Layuan, Cross-layer optimization policy for QoS scheduling in computational grid, J. Netw. Comput. Appl. 31 (2008), 258-284. (Pubitemid 351626657)
6. CometCloud, http://www.cometcloud.org.
7. K. Czajkowski, S. Fitzgerald, I. Foster and C. Kesselman, Grid information services for distributed resource sharing, in: Proceedings of the 10th IEEE International Symposium on High Performance Distributed Computing 2001, IEEE, Piscataway, NJ, USA, 2001, pp. 181-194. (Pubitemid 32806487)
8. M.D. de Assunção, A. di Costanzo and R. Buyya, Evaluating the cost-benefit of using cloud computing to extend the capacity of clusters, in: HPDC'09: Proceedings of the 18th ACM International Symposium on High Performance Distributed Computing, ACM, New York, NY, USA, 2009, pp. 141-150.
9. M.D. de Assunção, R. Buyya and S. Venugopal, Intergrid: A case for internetworking islands of grids, Concurrency and Computation: Practice and Experience 20(8) (2008), 997-1024.
10. J. Dean and S. Ghemawat, Mapreduce: Simplified data processing on large clusters, Commun. ACM 51(1) (2008), 107-113.
11. T. Dornemann, E. Juhnke and B. Freisleben, On-demand resource provisioning for BPEL workflows using Amazon's elastic compute cloud, in: CCGRID'09: Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, IEEE Computer Society, Washington, DC, USA, 2009, pp. 140-147.
12. Y.Y. El-Khamra, Real-time reservoir characterization and beyond: Cyberinfrastructure tools and technologies, Master's thesis, Louisiana State University, Baton Rouge, LA, 2009.
13. T. Freeman and K. Keahey, Flying low: Simple leases with workspace pilot, in: Euro-Par, 2008, pp. 499-509.
14. FutureGrid, http://www.futuregrid.org/.
15. Y. Gu and D.S. Oliver, The ensemble Kalman filter for continuous updating of reservoir simulation models, Journal of Engineering Resources Technology 128(1) (2006), 79-87. (Pubitemid 44058071)
16. Y. Gu and D.S. Oliver, An iterative ensemble Kalman filter for multiphase fluid flow data assimilation, SPE Journal 12(4) (2007), 438-446.
17. Hadoop, http://hadoop.apache.org/core/.
18. R.E. Kalman, A new approach to linear filtering and prediction problems, Transactions of the ASME - Journal of Basic Engineering 82(Series D) (1960), 35-45.
19. H. Kim, M. Parashar, D. Foran and L. Yang, Investigating the use of autonomic cloudbursts for high-throughput medical image registration, in: 10th IEEE/ACM International Conference on Grid Computing 2009, IEEE, Piscataway, NJ, USA, 2009, pp. 34-41.
20. H. Kim, Y. el Khamra, S. Jha and M. Parashar, An autonomic approach to integrated HPC grid and cloud usage, in: Fifth IEEE International Conference on e-Science, 2009, e-Science'09, December 2009, pp. 366-373.
21. H. Kim, S. Chaudhari, M. Parashar and C. Marty, Online risk analytics on the cloud, in: 9th IEEE/ACMInternational Symposium on Cluster Computing and the Grid, 2009, CCGRID'09, IEEE, Piscataway, NJ, USA, 2009, pp. 484-489.
22. X. Li, C.White, Z. Lei and G. Allen, Reservoir model updating by ensemble Kalman filter - practical approaches using grid computing technology, in: Petroleum Geostatistics 2007, Cascais, Portugal, August 2007.
23. P. Marshall, K. Keahey and T. Freeman, Elastic site: Using clouds to elastically extend site resources, in: IEEE International Symposium on Cluster Computing and the Grid, IEEE, Piscataway, NJ, USA, 2010, pp. 43-52.
24. P. Martinaitis, C. Patten and A. Wendelborn, Remote interaction and scheduling aspects of cloud based streams, in: 5th IEEE International Conference on E-Science Workshops, 2009, IEEE, Piscataway, NJ, USA, 2009, pp. 39-47.
25. L. Nie and Z. Xu, An adaptive scheduling mechanism for elastic grid computing, in: SKG'09: Proceedings of the 2009 Fifth International Conference on Semantics, Knowledge and Grid, IEEE Computer Society, Washington, DC, USA, 2009, pp. 184-191.
26. Open Nebula Project, http://www.opennebula.org/doku.php.
27. S. Ostermann, R. Prodan and T. Fahringer, Extending grids with cloud resource management for scientific computing, in: 10th IEEE/ACM International Conference on Grid Computing 2009, IEEE, Piscataway, NJ, USA, 2009, pp. 42-49.
28. A. Ozer and C. Ozturan, An auction based mathematical model and heuristics for resource co-allocation problem in grids and clouds, in: Fifth International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control, 2009, ICSCCW 2009, IEEE, Piscataway, NJ, USA, 2009, pp. 1-4.
29. C. Schmidt and M. Parashar, Squid: Enabling search in DHTbased systems, J. Parallel Distrib. Comput. 68(7) (2008), 962-975.
30. M. Siddiqui, A. Villazon, J. Hofer and T. Fahringer, Glare: A grid activity registration, deployment and provisioning framework, in: Proceedings of the ACM/IEEE SC 2005 Conference on Supercomputing 2005, IEEE, Piscataway, NJ, USA, 2005, p. 52.
31. A. Sodan, Adaptive scheduling for qos virtual machines under different resource allocation - first experiences, in: Job Scheduling Strategies for Parallel Processing, E. Frachtenberg and U. Schwiegelshohn, eds, Lecture Notes in Computer Science, Vol. 5798, Springer, Berlin/Heidelberg, pp. 259-279.
32. I. Stoica, R. Morris, D. Liben-Nowell, D.R. Karger, M.F. Kaashoek, F. Dabek and H. Balakrishnan, Chord: A scalable peer-to-peer lookup protocol for internet applications, in: ACM SIGCOMM, ACM, New York, NY, USA, 2001, pp. 149-160. (Pubitemid 32981961)
33. D. Thain, T. Tannenbaum and M. Livny, Distributed computing in practice: The condor experience, Concurrency and Computation: Practice and Experience 17 (2005), 2-4.
34. C. Vazquez, E. Huedo, R. Montero and I. Llorente, Dynamic provision of computing resources from grid infrastructures and cloud providers, in: Workshops at the Grid and Pervasive Computing Conference, 2009, GPC'09, IEEE, Piscataway, NJ, USA, 2009, pp. 113-120.