Towards an Adaptive High-performance Execution of Scientific Applications in a Dynamic Cloud Environment

International Journal of P2P Network Trends and Technology (IJPTT)          
© 2015 by IJPTT Journal
Volume - 5 Issue - 1
Year of Publication : 2015
Authors : Mohamed-K Hussein
DOI :  10.14445/22492615/IJPTT-V15P401


Mohamed-K HUSSEIN. "Towards an Adaptive High-performance Execution of Scientific Applications in a Dynamic Cloud Environment". International Journal of P2P Network Trends and Technology (IJPTT), V5(1) :1-7 Jan - Feb 2015, ISSN:2249-2615,, Published by Seventh Sense Research Group.


During the last decade, the needs for high-performance computing for distributed scientific applications have been addressed over multiple high-performance environments including clusters and Grid computing technologies. Recently, cloud computing technology offers cheap and large-scale high-performance computing environment. Infrastructure as a Service cloud (IaaS) offers instant access to large-scale computing resources. However, the performance of the resources can dynamically varies according to the changing load conditions on the resources. Further, scientific applications require complex communication/computation pattern, such as optimized MPI for communication. For these reasons, it is challenging to achieve high-performance in a cloud environment. This paper presents an initial framework towards achieving adaptive high-performance execution for a distributed scientific application over a private dynamic cloud environment. The adaptation is achieved by migrating the distributed components of the benchmark application, which suffer performance degradation, to a promising different resource. The proposed framework contains a monitoring layer which monitors the execution times of the running application’s components. A decision layer issues the migration decision considering the execution times and the cost of the migration. Finally, the paper presents the applicability of the proposed framework on a private IaaS cloud managed by Eucalyptus.


[1] 1. Jha, S., et al., Understanding Scientific Applications for Cloud Environments, in Cloud Computing. 2011, John Wiley & Sons, Inc. p. 345-371.
[2] 2. Foster, I. and C. Kesselman, The Grid 2: Blueprint for a New Computing Infrastructure. 2003: Morgan Kaufmann Publishers Inc.
[3] 3. Coveney, P.V., et al., Scientific Grid Computing: The First Generation. Computing in Science and Engg., 2005. 7(5): p. 24-32.
[4] 4. A grid-enabled web service for low-resolution crystal structure refinement. Acta Crystallographica Section D Biological Crystallography, 2012. 68(3): p. 261.
[5] 5. Pordes, R., et al., New science on the Open Science Grid. Journal of Physics: Conference Series, 2008. 125(1): p. 012070.
[6] 6. Pordes, R., t.O.S.G.E. Board, and J. Weichel, Analysis of the current use, benefit, and value of the Open Science Grid. Journal of Physics: Conference Series, 2010. 219(6): p. 062024.
[7] 7. Vecchiola, C., S. Pandey, and R. Buyya, High-Performance Cloud Computing: A View of Scientific Applications, in Proceedings of the 2009 10th International Symposium on Pervasive Systems, Algorithms, and Networks. 2009, IEEE Computer Society. p. 4-16.
[8] 8. Armbrust, M., et al., A view of cloud computing. Commun. ACM, 2010. 53(4): p. 50-58.
[9] 9. Buyya, R., et al., Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Gener. Comput. Syst., 2009. 25(6): p. 599-616.
[10] 10. Uhlig, R., et al., Intel Virtualization Technology. Computer, 2005. 38(5): p. 48-56.
[11] 11. Ekanayake, J. and G. Fox, High Performance Parallel Computing with Clouds and Cloud Technologies, in Cloud Computing, D. Avresky, et al., Editors. 2010, Springer Berlin Heidelberg. p. 20-38.
[12] 12. Burd, S.D., et al. Virtual Computing Laboratories Using VMware Lab Manager. in System Sciences (HICSS), 2011 44th Hawaii International Conference on. 2011.
[13] 13. Barham, P., et al., Xen and the art of virtualization. SIGOPS Oper. Syst. Rev., 2003. 37(5): p. 164-177.
[14] 14. Childers, B., Virtualization shootout: VMware server vs. VirutalBox vs. KVM. Linux J., 2009. 2009(187): p. 12.
[15] 15. Nurmi, D., et al., The Eucalyptus Open-Source Cloud-Computing System, in Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid. 2009, IEEE Computer Society. p. 124-131.
[16] 16. Kijsipongse, E. and S. Vannarat, Autonomic resource provisioning in rocks clusters using Eucalyptus cloud computing, in Proceedings of the International Conference on Management of Emergent Digital EcoSystems. 2010, ACM: Bangkok, Thailand. p. 61-66.
[17] 17. Tudoran, R., et al., A performance evaluation of Azure and Nimbus clouds for scientific applications, in Proceedings of the 2nd International Workshop on Cloud Computing Platforms. 2012, ACM: Bern, Switzerland. p. 1-6.
[18] 18. Sempolinski, P. and D. Thain, A Comparison and Critique of Eucalyptus, OpenNebula and Nimbus, in Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science. 2010, IEEE Computer Society. p. 417-426.
[19] 19. Raveendran, A., T. Bicer, and G. Agrawal. A Framework for Elastic Execution of Existing MPI Programs. in Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW), 2011 IEEE International Symposium on. 2011.
[20] 20. Deelman, E., et al., The cost of doing science on the cloud: the Montage example, in Proceedings of the 2008 ACM/IEEE conference on Supercomputing. 2008, IEEE Press: Austin, Texas. p. 1-12.
[21] 21. HUSSEIN, M.-K. and M.-H. MOUSA, High-performance Execution of Scientific Multi-Physics Coupled Applications in a Private Cloud. International Journal of Advanced Research in Computer Science and Software Engineering, 2014. 4(2): p. 11-16.
[22] 22. Hussein, M., et al., Adaptive performance control for distributed scientific coupled models, in Proceedings of the 21st annual international conference on Supercomputing. 2007, ACM: Seattle, Washington. p. 274-283.
[23] 23. Youseff, L., et al., Evaluating the Performance Impact of Xen on MPI and Process Execution For HPC Systems, in Proceedings of the 2nd International Workshop on Virtualization Technology in Distributed Computing. 2006, IEEE Computer Society. p. 1.
[24] 24. Galante, G. and L.C.E. Bona, Supporting Elasticity in OpenMP Applications, in Proceedings of the 2014 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing. 2014, IEEE Computer Society. p. 188-195.
[25] 25. Chieu, T.C., et al., Dynamic Scaling of Web Applications in a Virtualized Cloud Computing Environment, in Proceedings of the 2009 IEEE International Conference on e-Business Engineering. 2009, IEEE Computer Society. p. 281-286.
[26] 26. Galante, G. and L.C.E. de Bona. A Survey on Cloud Computing Elasticity. in Utility and Cloud Computing (UCC), 2012 IEEE Fifth International Conference on. 2012.
[27] 27. Roy, N., A. Dubey, and A. Gokhale, Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting, in Proceedings of the 2011 IEEE 4th International Conference on Cloud Computing. 2011, IEEE Computer Society. p. 500-507.
[28] 28. Murugavel, S.S., S.S. Vadhiyar, and R.S. Nanjundiah, Adaptive Executions of Multi-Physics Coupled Applications on Batch Grids. J. Grid Comput., 2011. 9(4): p. 455-478.
[29] 29. Goglin, B., High-performance message-passing over generic Ethernet hardware with Open-MX. Parallel Comput., 2011. 37(2): p. 85-100.

Scientific computing, Cloud computing, high-performance computing, MPI applications, Eucalyptus, Adaptive execution.