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Performance Evaluation of Multiple Cloud Data Centers Allocations for HPC

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High Performance Computing (CARLA 2016)

Abstract

This paper evaluates the behavior of the Microsoft Azure G5 cloud instance type over multiple Data Centers. The purpose is to identify if there are major differences between them and to help the users choose the best option for their needs. Our results show that there are differences in the network level for the same instance type in different locations and inside the same location at different times. The network performance causes interference in the applications level, as we could verify in our results.

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References

  1. Awad, O.M.O., Artoli, A.M.A., Ahmed, A.H.A.: Cloud computing versus in-house clusters: a comparative study. In: 2014 World Congress on Computer Applications and Information Systems (WCCAIS), pp. 1–6, January 2014

    Google Scholar 

  2. Ekanayake, J., Fox, G.: High performance parallel computing with clouds and cloud technologies. In: Avresky, D.R., Diaz, M., Bode, A., Ciciani, B., Dekel, E. (eds.) CloudComp 2009. LNICSSTE, vol. 34, pp. 20–38. Springer, Heidelberg (2010). doi:10.1007/978-3-642-12636-9_2

    Chapter  Google Scholar 

  3. He, Q., Zhou, S., Kobler, B., Duffy, D., McGlynn, T.: Case study for running HPC applications in public clouds. In: Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing, HPDC 2010, pp. 395–401. ACM, New York (2010). http://doi.acm.org/10.1145/1851476.1851535

  4. Intel MPI Benchmarks: User Guide and Methodology Description (2014)

    Google Scholar 

  5. Iosup, A., Ostermann, S., Yigitbasi, M.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)

    Article  Google Scholar 

  6. Marathe, A., Harris, R., Lowenthal, D.K., de Supinski, B.R., Rountree, B., Schulz, M., Yuan, X.: A comparative study of high-performance computing on the cloud. In: Proceedings of the 22nd International Symposium on High-Performance Parallel and Distributed Computing, HPDC 2013, pp. 239–250. ACM, New York (2013). http://doi.acm.org/10.1145/2462902.2462919

  7. da Rosa Righi, R., Rodrigues, V.F., da Costa, C.A., Galante, G., de Bona, L.C.E., Ferreto, T.: Autoelastic: automatic resource elasticity for high performance applications in the cloud. IEEE Trans. Cloud Comput. 4(1), 6–19 (2016)

    Article  Google Scholar 

  8. Vázquez, M., Houzeaux, G., Rubio, F., Simarro, C.: Alya multiphysics simulations on Intel’s Xeon Phi accelerators. In: Hernández, G., Barrios Hernández, C.J., Díaz, G., García Garino, C., Nesmachnow, S., Pérez-Acle, T., Storti, M., Vázquez, M. (eds.) CARLA 2014. CCIS, vol. 485, pp. 248–254. Springer, Heidelberg (2014). doi:10.1007/978-3-662-45483-1_18

    Google Scholar 

  9. Vázquez, M., Houzeaux, G., Koric, S., Artigues, A., Aguado-Sierra, J., Arís, R., Mira, D., Calmet, H., Cucchietti, F., Owen, H., Taha, A., Burness, E.D., Cela, J.M., Valero, M.: Alya: multiphysics engineering simulation toward exascale. J. Comput. Sci. 14, 15–27 (2016). The Route to Exascale: Novel Mathe-matical Methods, Scalable Algorithms and Computational Science Skills. http://www.sciencedirect.com/science/article/pii/S1877750315300521

    Article  MathSciNet  Google Scholar 

  10. Zounmevo, J.A., Kimpe, D., Ross, R., Afsahi, A.: Using MPI in high-performance computing services. In: Proceedings of the 20th European MPI Users’ Group Meeting, EuroMPI 2013, pp. 43–48. ACM, New York (2013). http://doi.acm.org/10.1145/2488551.2488556

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Acknowledgments

This research received funding from the EU H2020 Programme and from MCTI/RNP-Brazil under the HPC4E project, grant agreement no. 689772. Experiments presented in this paper were carried out using the Grid’5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see https://www.grid5000.fr). Additional funding was provided by CAPES and Microsoft.

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Correspondence to Eduardo Roloff .

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Roloff, E. et al. (2017). Performance Evaluation of Multiple Cloud Data Centers Allocations for HPC. In: Barrios Hernández, C., Gitler, I., Klapp, J. (eds) High Performance Computing. CARLA 2016. Communications in Computer and Information Science, vol 697. Springer, Cham. https://doi.org/10.1007/978-3-319-57972-6_2

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  • DOI: https://doi.org/10.1007/978-3-319-57972-6_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57971-9

  • Online ISBN: 978-3-319-57972-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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