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Towards Cross-Platform Cloud Computing

  • Magdalena Slawinska
  • Jaroslaw Slawinski
  • Vaidy Sunderam
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7155)

Abstract

Cloud computing is becoming increasingly popular and prevalent in many domains. However, there is high variability in the programming models, access methods, and operational aspects of different clouds, diminishing the viability of cloud computing as a true utility. Our ADAPAS project attempts to analyze the commonalities and differences between cloud offerings with a view to determining the extent to which they may be unified. We propose the concept of dynamic adapters supported by runtime systems for environment preconditioning, that help facilitate cross platform deployment of cloud applications. This vision paper outlines the issues involved, and presents preliminary ideas for enhancing the executability of applications on different cloud platforms.

Keywords

Cloud Computing Cloud Platform Programming Paradigm Application Execution Execution Context 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References

  1. 1.
    Amazon Elastic MapReduce (2011), http://aws.amazon.com/elasticmapreduce/
  2. 2.
    PortableApps.com web page (2011), http://portableapps.com/
  3. 3.
    rPath Documentation (2011), http://docs.rpath.com/
  4. 4.
    The Simple Cloud API (2011), http://www.simplecloudapi.org/
  5. 5.
    Avetisyan, A.I., Campbell, R., Gupta, I., Heath, M.T., Ko, S.Y., Ganger, G.R., Kozuch, M.A., O’Hallaron, D., Kunze, M., Kwan, T.T., Lai, K., Lyons, M., Milojicic, D.S., Lee, H.Y., Soh, Y.C., Ming, N.K., Luke, J.-Y., Namgoong, H.: Open Cirrus: A Global Cloud Computing Testbed. Computer 43, 35–43 (2010)CrossRefGoogle Scholar
  6. 6.
    Bailey, D., Barszcz, E., Barton, J., Browning, D., Carter, R., Dagum, L., Fatoohi, R., Frederickson, P., Lasinski, T., Schreiber, R., et al.: The NAS Parallel Benchmarks. International Journal of HPC Apps 5(3), 63 (1991)Google Scholar
  7. 7.
    Chappell, D.: Introducing Windows Azure. DavidChappell & Associates (December 2009), Sponsored by Microsoft CorporationGoogle Scholar
  8. 8.
    Chohan, N., Bunch, C., Pang, S., Krintz, C., Mostafa, N., Soman, S., Wolski, R.: AppScale Design and Implementation. Technical report, UCSB Technical Report Number 2009 (2009)Google Scholar
  9. 9.
    Gardenghi, L., Goldweber, M., Davoli, R.: View-OS: A New Unifying Approach Against the Global View Assumption. In: Bubak, M., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2008, Part I. LNCS, vol. 5101, pp. 287–296. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Gropp, W.D.: MPICH2: A New Start for MPI Implementations. In: Kranzlmüller, D., Kacsuk, P., Dongarra, J., Volkert, J. (eds.) PVM/MPI 2002. LNCS, vol. 2474, p. 7. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  11. 11.
    Keahey, K., Figueiredo, R., Fortes, J., Freeman, T., Tsugawa, M.: Science Clouds: Early Experiences in Cloud Computing for Scientific Applications. In: Cloud Computing and Its Application (CCA 2008) (October 2008)Google Scholar
  12. 12.
    Kim, H., el Khamra, Y., Jha, S., Parashar, M.: An autonomic approach to integrated hpc grid and cloud usage. In: Fifth IEEE International Conference on e-Science 2009, pp. 366–373. IEEE (2009)Google Scholar
  13. 13.
    Kozuch, M., Ryan, M., Gass, R., Schlosser, S., O’Hallaron, D., Cipar, J., Krevat, E., López, J., Stroucken, M., Ganger, G.: Tashi: location-aware cluster management. In: Proceedings of the 1st Workshop on Automated Control for Datacenters and Clouds, pp. 43–48. ACM (2009)Google Scholar
  14. 14.
    Kurzyniec, D., Sunderam, V.: Combining FT-MPI with H2O: Fault-tolerant MPI across administrative boundaries. In: Proceedings of 19th IEEE International Parallel and Distributed Processing Symposium, pp. 120a–120a (2005)Google Scholar
  15. 15.
    Nurmi, D., Wolski, R., Grzegorczyk, C., Obertelli, G., Soman, S., Youseff, L., Zagorodnov, D.: The Eucalyptus Open-source Cloud-computing System. In: 9th IEEE International Symposium on Cluster Computing and the Grid, Shanghai, China (2009)Google Scholar
  16. 16.
    Reid, J., Numrich, R.W.: Co-arrays in the next Fortran Standard. Sci. Program 15(1), 9–26 (2007)Google Scholar
  17. 17.
    Rochkind, M.J.: Advanced UNIX programming. Prentice-Hall, Inc., Upper Saddle River (1985)Google Scholar
  18. 18.
    Severance, C.: Using Google App Engine. O’Reilly Media (May 2009)Google Scholar
  19. 19.
    Skomoroch, P.: MPI Cluster Programming with Python and Amazon EC2. In: PyCon 2008, Chicago (2008)Google Scholar
  20. 20.
    Slawinski, J., Slawinska, M., Sunderam, V.: The Unibus Approach to Provisioning Software Applications on Diverse Computing Resources. In: International Conference On High Performance Computing, 3rd International Workshop on Service Oriented Computing (December 2009)Google Scholar
  21. 21.
    Varia, J.: Cloud architectures. Technical report, Amazon Web Services, White Paper (2008)Google Scholar
  22. 22.
    Vecchiola, C., Pandey, S., Buyya, R.: High-performance cloud computing: A view of scientific applications. In: 10th International Symposium on Pervasive Systems, Algorithms, and Networks, pp. 4–16. IEEE (2009)Google Scholar
  23. 23.
    White, T.: Hadoop: The Definitive Guide. O’Reilly Media (May 2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Magdalena Slawinska
    • 1
  • Jaroslaw Slawinski
    • 1
  • Vaidy Sunderam
    • 1
  1. 1.Department of Mathematics and Computer ScienceEmory UniversityAtlantaUSA

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