Evolving multi-objective strategies for task allocation of scientific workflows on public clouds

2012 IEEE Congress on Evolutionary Computation, CEC 2012

Volumn , Issue , 2012, Pages

  Szabo, Claudia ^a   Kroeger, Trent ^a  

^a UNIVERSITY OF ADELAIDE   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

COMMUNICATION OVERHEADS; COST MODELS; MULTI OBJECTIVE OPTIMIZATIONS (MOO); MULTI-OBJECTIVE STRATEGIES; NP COMPLETE; OPTIMAL NUMBER; OPTIMIZATION FRAMEWORK; OPTIMIZATION HEURISTICS; OVERALL EXECUTION; PARTICLE SWARM OPTIMIZATION APPROACHES; PRIVATE CLOUDS; RUNTIMES; SCIENTIFIC APPLICATIONS; SCIENTIFIC WORKFLOWS; SINGLE OBJECTIVE OPTIMIZATION; STATE-OF-THE-ART APPROACH; STORAGE SERVICES; TASK ALLOCATION; WORK-FLOWS;

PARTICLE SWARM OPTIMIZATION (PSO);

MULTIOBJECTIVE OPTIMIZATION;

EID: 84866872524     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2012.6256556     Document Type: Conference Paper

References (26)

1. D. Abramson, C. Enticott, and I. Altinas. Nimrod/K: Towards Massively Parallel Dynamic Grid Workflows. In Proceedings of the ACM/IEEE Conference on Supercomputing, 2008.
2. Amazon. Best Practices for Using Amazon S3. http://aws.amazon.com/ articles/Amazon-S3/1904, (Jan. 2012).
3. Arstechnica. $1,279 per Hour, 30,000-core Cluster Built on Amazon EC2 Cloud. http://arstechnica.com/business/news/2011/09/30000-core-cluster- built-on-amazon-ec2-cloud.ars, last retrieved Jan. 2012.
4. S. Bharathi, A. Chervenak, E. Deelman, G. Mehta, M.-H. Su, and K. Vahi. Characterization of Scientic Workows. In Proceedings of the Third Workshop on Workows in Support of Large-Scale Science, pages 1-10, 2008.
5. U. V. Catalyuek, K. Kaya, and B. Ucar. Integrated Data Placement and Task Assignment for Scientific Workflows in Clouds. In Proceedings of the Fourth International Workshop on Data Intensive Distributed Computing, 2009.
6. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. Evolutionary Computation, IEEE Transactions on, 6(2):182-197, 2002.
7. E. Deelman, G. Singh, M.-H. Su, J. Blythe, Y. Gil, C. Kesselman, G. Mehta, K. Vahi, G. B. Berriman, J. Good, A. Laity, J. C. Jacob, and D. S. Katz. Pegasus: A Framework for Mapping Complex Scientific Workflows onto Distributed Systems. Scientific Programming, 13:219-237, 2005.
8. J. Durillo, A. Nebro, F. Luna, B. Dorronsoro, and E. Alba. jmetal: a java framework for developing multi-objective optimization metaheuristics. Departamento de Lenguajes y Ciencias de la Computación, University of Málaga, ETSI Informática, Campus de Teatinos, Tech. Rep. ITI-2006-10, 2006.
9. C. Evangelinos and C. N. Hill. Cloud Computing for Parallel Scientific HPC Applications: Feasibility of running Coupled Atmosphere-Ocean Climate Models on Amazons EC2. In Cloud Computing and its Applications, 2008.
10. D. Fernandez-Baca. Allocating Modules to Processors in a Distributed System. IEEE Transaction on Software Engineering, 15:1427-1436, 1989.
11. JSwarm. PSO Optimization Package. http://jswarm-pso.sourceforge.net/, last retrieved Jan. 2012.
12. G. Juve, E. Deelman, K. Vahi, G. Mehta, B. P. Berman, B. Berriman, and P. Maechling. Scientific Workflow Applications on Amazon EC2. Grids, Clouds and Virtualization, pages 71-91, 2010.
13. D. S. Katz, J. C. Jacob, G. Berriman, J. Good, A. C. Laity, E. Deelman, C. Kesselman, G. Singh, M.-H. Su, and T. A. Prince. A Comparison of Two Methods for Building Astronomical Image Mosaics on a Grid. In Proceedings of the International Conference on Parallel Processing, pages 85-94, 2005.
14. J. Kennedy and R. Eberhart. Particle Swarm Optimization. In Proceedings of the IEEE International Conference on Neural Networks, pages 1942-1948, 1995.
15. Y.-K. Kwok and I. Ahmad. Efficient Scheduling of Arbitrary Task Graphs to Multiprocessors Using a Parallel Genetic Algorithm. Journal of Parallel and Distributed Computing, 47:58-77, 1997.
16. B. Ludascher, I. Altintas, C. Berkley, D. Higgins, E. Jaeger, M. Jones, E. A. Lee, J. Tao, and Y. Zhao. Scientific Workflow Management and the Kepler System. Concurrency and Computation: Practice and Experience, 18:1039-1065, 2006.
17. N. I. of Standards and Technology. Cloud Computing Synopsis and Recommendations. http://csrc.nist.gov/publications/drafts/800-146/Draft- NIST-SP800-146.pdf, last retrieved Jan. 2012.
18. T. Oinn, M. Addis, J. Ferris, D. Marvin, M. Senger, M. Greenwood, T. Carver, K. Glover, M. R. Pocock, A. Wipat, and P. Li. Taverna: a Tool for the Composition and Enactment of Bioinformatics Workflows. Bioinformatics, 20:3045-3054, 2004.
19. S. Pandey, L. Wu, S. Guru, and R. Buyya. A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In Proceedings of the 24th IEEE International Conference on Advanced Information Networking and Applications, pages 400-407, 2010.
20. Pegasus Project. Workflow Generator. https://confluence.pegasus.isi.edu/ display/pegasus/WorkflowGenerator, last retrieved Jan. 2012.
21. T. Shibata, S. Choi, and K. Taura. File-access Characteristics of Data-intensive Workflow Applications. In Proceedings of the 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, pages 522-525, 2010.
22. J.-S. Vckler, G. Juve, E. Deelman, M. Rynge, and G. B. Berriman. Experiences Using Cloud Computing for A Scientific Workflow Application. In Proceedings of 2nd Workshop on Scientific Cloud Computing, 2011.
23. E. Walker. Benchmarking Amazon EC2 for High-Performance Scientific Computing. USENIX Login, 33:18-23, 2008.
24. Z. Wu, Z. Ni, L. Gu, and X. Liu. A Revised Discrete Particle Swarm Optimization for Cloud Workflow Scheduling. In International Conference on Computational Intelligence and Security, pages 184-188, 2010.
25. N. Yigitbasi, A. Iosup, and D. Epema. C-Meter: A Framework for Performance Analysis of Computing Clouds. In Cluster Computing and the Grid, pages 472-477, 2009.
26. D. Yuan, Y. Yang, X. Liu, and J. Chen. A Data Placement Strategy in Scientific Cloud Workflows. Future Generation Computing Systems, 26:1200-1214, 2010.