A Reliable and Scalable Service Bus Based on Amazon SQS

  • Sergio Hernández
  • Javier Fabra
  • Pedro Álvarez
  • Joaquín Ezpeleta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8135)

Abstract

Cloud computing infrastructures are becoming a very powerful mean for the implementation of reliable and extensible computing systems. In this paper, we evaluate the viability of migrating a framework for the execution of (scientific) workflows from a cluster-based to a cloud-supported implementation. As a first step, we focus on the viability of adapting the framework message bus (which has a Linda semantics) to the use of the Amazon Simple Queue Service (Amazon SQS). The paper evaluates the performance of the cloud-based bus and studies the influence of the network latency, depending on different geographical locations and configurations. It also compares the cloud-based bus with DRLinda, our former implementation, in terms of economic cost and performance. This comparison allows us to conclude that, under the same conditions, the cloud-based message bus is faster, more scalable and more reliable.

Keywords

Cloud based interoperation Cost evaluation Web service based coordination 

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References

  1. 1.
    Vaquero, L.M., Rodero-Merino, L., Caceres, J., Lindner, M.: A break in the clouds: towards a cloud definition. SIGCOMM Comput. Commun. Rev. 39(1), 50–55 (2008)CrossRefGoogle Scholar
  2. 2.
    Liu, H.: Cutting mapreduce cost with spot market. In: The 3rd USENIX Conference on Hot topics in Cloud Computing, HotCloud 2011 (2011)Google Scholar
  3. 3.
    Yoon, H., Gavrilovska, A., Schwan, K., Donahue, J.: Interactive use of cloud services: Amazon sqs and s3. In: The 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2012, pp. 523–530 (2012)Google Scholar
  4. 4.
    Jackson, K.R., Ramakrishnan, L., Muriki, K., Canon, S., Cholia, S., Shalf, J., Wasserman, H.J., Wright, N.J.: Performance analysis of high performance computing applications on the amazon web services cloud. In: The 2010 IEEE Second International Conference on Cloud Computing Technology and Science, CLOUDCOM 2010, pp. 159–168 (2010)Google Scholar
  5. 5.
    Losup, A., Ostermann, S., Yigitbasi, N., Prodan, R., Fahringer, T., Epema, D.: Performance analysis of cloud computing services for many-tasks scientific computing. IEEE Trans. Parallel Distrib. Syst. 22(6), 931–945 (2011)CrossRefGoogle Scholar
  6. 6.
    Yu, J., Buyya, R.: A taxonomy of workflow management systems for grid computing. J. Grid Comput. 3(3-4), 171–200 (2005)CrossRefGoogle Scholar
  7. 7.
    Rahman, M., Ranjan, R., Buyya, R., Benatallah, B.: A taxonomy and survey on autonomic management of applications in grid computing environments. Concur. Comput.: Pract. Exper. 23(16), 1990–2019 (2011)CrossRefGoogle Scholar
  8. 8.
    Fabra, J., Hernández, S., Álvarez, P., Ezpeleta, J.: A framework for the flexible deployment of scientific workflows in grid environments. In: The Third International Conference on Cloud Computing, GRIDs, and Virtualization, CLOUD COMPUTING 2012, pp. 1–8 (2012)Google Scholar
  9. 9.
    Amazon Web Services (2012), http://aws.amazon.com (accessed May 1, 2013)
  10. 10.
    Hernández, S., Fabra, J., Álvarez, P., Ezpeleta, J.: A Simulation-based Scheduling Strategy for Scientific Workflows. In: The 2nd International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2012, pp. 61–70 (2012)Google Scholar
  11. 11.
    Carriero, N., Gelernter, D.: Linda in context. Commun. ACM 32(4), 444–458 (1989)CrossRefGoogle Scholar
  12. 12.
    Fabra, J., Álvarez, P., Ezpeleta, J.: DRLinda: A Distributed Message Broker for Collaborative Interactions Among Business Processes. In: Psaila, G., Wagner, R. (eds.) EC-Web 2007. LNCS, vol. 4655, pp. 212–221. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Hernández, S., Fabra, J., Álvarez, P., Ezpeleta, J.: Using cloud-based resources to improve availability and reliability in a scientific workflow execution framework. In: The Fourth International Conference on Cloud Computing, GRIDs, and Virtualization, CLOUD COMPUTING 2013, pp. 230–237 (2013)Google Scholar
  14. 14.
    Ostermann, S., Prodan, R., Fahringer, T.: Extending grids with cloud resource management for scientific computing. In: The 10th IEEE/ACM International Conference on Grid Computing, pp. 42–49 (2009)Google Scholar
  15. 15.
    Palankar, M.R., Iamnitchi, A., Ripeanu, M., Garfinkel, S.: Amazon s3 for science grids: a viable solution? In: The 2008 International Workshop on Data-Aware Distributed Computing, DADC 2008, pp. 55–64 (2008)Google Scholar
  16. 16.
    Deelman, E., Singh, G., Livny, M., Berriman, B., Good, J.: The cost of doing science on the cloud: The montage example. In: the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2008, pp. 1–12 (2008)Google Scholar
  17. 17.
    Garfinkel, S.L.: An evaluation of amazons grid computing services: Ec2, s3 and sqs. Technical report. Center for Research on Computation and Society (2007)Google Scholar
  18. 18.
    Fabra, J., Álvarez, P., Bañares, J.A., Ezpeleta, J.: RLinda: A Petri Net Based Implementation of the Linda Coordination Paradigm for Web Services Interactions. In: Bauknecht, K., Pröll, B., Werthner, H. (eds.) EC-Web 2006. LNCS, vol. 4082, pp. 183–192. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sergio Hernández
    • 1
  • Javier Fabra
    • 1
  • Pedro Álvarez
    • 1
  • Joaquín Ezpeleta
    • 1
  1. 1.Aragón Institute of Engineering Research (I3A) Department of Computer Science and Systems EngineeringUniversity of ZaragozaSpain

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