Skip to main content

Constructing Elastic Scientific Applications Using Elasticity Primitives

  • Conference paper
Computational Science and Its Applications – ICCSA 2013 (ICCSA 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7975))

Included in the following conference series:

Abstract

Elasticity can be seen as the ability of a system to increase or decrease the computing resources allocated in a dynamic and on demand way. Considering its importance, some mechanisms to explore elasticity have been proposed by public providers and by academy. However, these solutions are inappropriate to provide elasticity for scientific applications or are limited to a specific programming model. In this context, we present Cloudine, a platform for development of elastic scientific applications based in simple elasticity primitives. These primitives enable the dynamic allocation and deallocation of resources in several levels, ranging from nodes of a virtual cluster, to virtual processors and memory of a node. Using this basic building blocks it is possible to develop applications in different models. The Cloudine effectiveness is demonstrated in the experiments, where two elastic applications in different models were developed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Villamizar, M., Castro, H., Mendez, D.: E-Clouds: A SaaS Marketplace for Scientific Computing. In: Proceedings of the 2012 IEEE/ACM Fifth International Conference on Utility and Cloud Computing, UCC 2012, pp. 13–20. IEEE Computer Society (2012)

    Google Scholar 

  2. Badger, L., Patt-Corner, R., Voas, J.: DRAFT Cloud Computing Synopsis and Recommendations Recommendations of the National Institute of Standards and Technology. Nist Special Publication 146 (2011)

    Google Scholar 

  3. Leymann, F.: Cloud Computing: The Next Revolution in IT. In: Proc. 52th Photogrammetric Week, pp. 3–12. Wichmann (September 2009)

    Google Scholar 

  4. Chieu, T.C., Mohindra, A., Karve, A.A., Segal, A.: Dynamic Scaling of Web Applications in a Virtualized Cloud Computing Environment. In: Proceedings of the 2009 IEEE International Conference on e-Business Engineering, ICEBE 2009, pp. 281–286. IEEE (2009)

    Google Scholar 

  5. Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, A., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., Zaharia, M.: A View of Cloud Computing. Commun. ACM 53(4) (April 2010)

    Google Scholar 

  6. Wang, L., Zhan, J., Shi, W., Liang, Y.: In Cloud, Can Scientific Communities Benefit from the Economies of Scale? IEEE Transactions on Parallel and Distributed Systems 23(2), 296–303 (2012)

    Article  Google Scholar 

  7. Oliveira, D., Baio, F.A., Mattoso, M.: Migrating Scientific Experiments to the Cloud. HPC in the Cloud, http://www.hpcinthecloud.com/hpccloud/2011-03-04/migrating_scientific_expe

  8. Galante, G., Bona, L.C.E.: A Survey on Cloud Computing Elasticity. In: Proceedings of the International Workshop on Clouds and eScience Applications Management, CloudAM 2012, pp. 263–270. IEEE/ACM (2012)

    Google Scholar 

  9. Chohan, N., Castillo, C., Spreitzer, M., Steinder, M., Tantawi, A., Krintz, C.: See Spot Run: Using Spot Instances for Mapreduce Workflows. In: Proceedings of the 2nd USENIX conference on Hot Topics in Cloud Computing, HotCloud 2010. USENIX Association (2010)

    Google Scholar 

  10. Amazon Web Services, http://aws.amazon.com/

  11. Microsoft Azure, http://www.windowsazure.com/

  12. Rackspace, http://www.rackspace.com/

  13. Vaquero, L.M., Rodero-Merino, L., Buyya, R.: Dynamically Scaling Applications in the Cloud. SIGCOMM Comput. Commun. Rev. 41, 45–52 (2011)

    Article  Google Scholar 

  14. Byun, E.K., Kee, Y.S., Kim, J.S., Maeng, S.: Cost Optimized Provisioning of Elastic Resources for Application Workflows. Future Gener. Comput. Syst. 27(8), 1011–1026 (2011)

    Article  Google Scholar 

  15. Iordache, A., Morin, C., Parlavantzas, N., Riteau, P.: Resilin: Elastic MapReduce over Multiple Clouds. Technical Report RR-8081, INRIA (2012)

    Google Scholar 

  16. Raveendran, A., Bicer, T., Agrawal, G.: A Framework for Elastic Execution of Existing MPI Programs. In: International Symposium on Parallel and Distributed Processing Workshops and PhD Forum, IPDPSW 2011, pp. 940–947. IEEE (2011)

    Google Scholar 

  17. Rajan, D., Canino, A., Izaguirre, J.A., Thain, D.: Converting a High Performance Application to an Elastic Cloud Application. In: 3rd International Conference on Cloud Computing Technology and Science, CLOUDCOM 2011, pp. 383–390. IEEE (2011)

    Google Scholar 

  18. GoGrid, http://www.gogrid.com/

  19. Caron, E., Rodero-Merino, L.F., Desprez, A.M.: Auto-Scaling, Load Balancing and Monitoring in Commercial and Open-Source Clouds. Technical Report 7857, INRIA (2012)

    Google Scholar 

  20. Google App Engine, http://code.google.com/appengine

  21. Lim, H.C., Babu, S., Chase, J.S., Parekh, S.S.: Automated Control in Cloud Computing: Challenges and Opportunities. In: 1st Workshop on Automated Control for Datacenters and Clouds, ACDC 2009, pp. 13–18. ACM (2009)

    Google Scholar 

  22. Roy, N., Dubey, A., Gokhale, A.: Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting. In: 4th International Conference on Cloud Computing, CLOUD 2011, pp. 500–507. IEEE (2011)

    Google Scholar 

  23. Gong, Z., Gu, X., Wilkes, J.: PRESS: PRedictive Elastic ReSource Scaling for Cloud Systems. In: 6th International Conference on Network and Service Management, CNSM 2010, pp. 9–16. IEEE (2010)

    Google Scholar 

  24. Sharma, U., Shenoy, P., Sahu, S., Shaikh, A.: A Cost-Aware Elasticity Provisioning System for the Cloud. In: Proceedings of the 31st International Conference on Distributed Computing Systems, ICDCS 2011, pp. 559–570. IEEE (2011)

    Google Scholar 

  25. Calheiros, R.N., Vecchiola, C., Karunamoorthy, D., Buyya, R.: The Aneka Platform and Qos-Driven Resource Provisioning for Elastic Applications on Hybrid Clouds. Future Generation Computer Systems 28(6), 861–870 (2011)

    Article  Google Scholar 

  26. Yu, L., Thain, D.: Resource Management for Elastic Cloud Workflows. In: Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2012, pp. 775–780. IEEE (2012)

    Google Scholar 

  27. OpenNebula, http://www.opennebula.org/

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Galante, G., Bona, L.C.E. (2013). Constructing Elastic Scientific Applications Using Elasticity Primitives. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2013. ICCSA 2013. Lecture Notes in Computer Science, vol 7975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39640-3_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-39640-3_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39639-7

  • Online ISBN: 978-3-642-39640-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics