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Data Center Clustering for Geographically Distributed Cloud Deployments

  • Dipesh Pradhan
  • Feroz ZahidEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 927)

Abstract

Geographically-distributed application deployments are critical for a variety of cloud applications, such as those employed in the Internet-of-Things (IoT), edge computing, and multimedia. However, selecting appropriate cloud data centers for the applications, from a large number available locations, is a difficult task. The users need to consider several different aspects in the data center selection, such as inter-data center network performance, data transfer costs, and the application requirements with respect to the network performance.

This paper proposes a data center clustering mechanism to group befitting cloud data centers together in order to automate data center selection task as governed by the application needs. Employing our clustering mechanism, we present four different types of clustering schemes, with different importance given to available bandwidth, latency, and cloud costs between pair of data centers. The proposed clustering schemes are evaluated using a large number of data centers from two major public clouds, Amazon Web Services, and Google Cloud Platform. The results, based on a comprehensive empirical evaluation of the quality of obtained clusters, show that the proposed clustering schemes are very effective in optimizing data center selection as per the application requirements.

Notes

Acknowledgement

This work has received funding from the European Union’s H2020 programme under grant agreement no. 731664 (MELODIC).

References

  1. 1.
    Liu, Y., Muppala, J.K., Veeraraghavan, M., Lin, D., Hamdi, M.: Data Center Networks: Topologies, Architectures and Fault-Tolerance Characteristics. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  2. 2.
    Khethavath, P., Thomas, J.P., Chan-tin, E.: Towards an efficient distributed cloud computing architecture. Peer-to-Peer Netw. Appl. 10(5), 1152–1168 (2017)CrossRefGoogle Scholar
  3. 3.
    Devi, R.K., Murugaboopathi, G.: An efficient clustering and load balancing of distributed cloud data centers using graph theory. Int. J. Commun. Syst. e3896 (2019)Google Scholar
  4. 4.
    Karypis, G., Han, E.H., Kumar, V.: Chameleon: hierarchical clustering using dynamic modeling. Computer 32(8), 68–75 (1999)CrossRefGoogle Scholar
  5. 5.
    Velmurugan, T.: Performance based analysis between k-Means and Fuzzy C-Means clustering algorithms for connection oriented telecommunication data. Appl. Soft Comput. 19, 134–146 (2014)CrossRefGoogle Scholar
  6. 6.
    Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis, vol. 344. Wiley, Hoboken (2009)zbMATHGoogle Scholar
  7. 7.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. (CSUR) 31(3), 264–323 (1999)CrossRefGoogle Scholar
  8. 8.
    Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315(5814), 972–976 (2007)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Lloyd, S.: Least squares quantization in PCM. IEEE Trans. Inf. Theory 28(2), 129–137 (1982)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Schubert, E., Koos, A., Emrich, T., Züfle, A., Schmid, K.A., Zimek, A.: A framework for clustering uncertain data. Proc. VLDB Endow. 8(12), 1976–1979 (2015)CrossRefGoogle Scholar
  11. 11.
    Aazam, M., Khan, I., Alsaffar, A.A., Huh, E.N.: Cloud of Things: Integrating Internet of Things and cloud computing and the issues involved. In: 11th International Bhurban Conference on Applied Sciences and Technology, IBCAST, pp. 414–419. IEEE (2014)Google Scholar
  12. 12.
    Shi, W., Dustdar, S.: The promise of edge computing. Computer 49(5), 78–81 (2016)CrossRefGoogle Scholar
  13. 13.
    Zhu, W., Luo, C., Wang, J., Li, S.: Multimedia cloud computing. IEEE Signal Process. Mag. 28(3), 59–69 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Simula Research LaboratoryFornebuNorway

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