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Data Center Clustering for Geographically Distributed Cloud Deployments

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 927)


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.

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  • DOI: 10.1007/978-3-030-15035-8_101
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    Cost-based clustering uses a weighted function equals to \(0.75 * Cost + 0.125 * Bandwidth + 0.125 * Latency\) to avoid ending up with a single big cluster in case of uniform traffic charges.


  1. Liu, Y., Muppala, J.K., Veeraraghavan, M., Lin, D., Hamdi, M.: Data Center Networks: Topologies, Architectures and Fault-Tolerance Characteristics. Springer, Heidelberg (2013)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  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. Karypis, G., Han, E.H., Kumar, V.: Chameleon: hierarchical clustering using dynamic modeling. Computer 32(8), 68–75 (1999)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  6. Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis, vol. 344. Wiley, Hoboken (2009)

    MATH  Google Scholar 

  7. Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. (CSUR) 31(3), 264–323 (1999)

    CrossRef  Google Scholar 

  8. Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315(5814), 972–976 (2007)

    CrossRef  MathSciNet  Google Scholar 

  9. Lloyd, S.: Least squares quantization in PCM. IEEE Trans. Inf. Theory 28(2), 129–137 (1982)

    CrossRef  MathSciNet  Google Scholar 

  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)

    CrossRef  Google Scholar 

  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. Shi, W., Dustdar, S.: The promise of edge computing. Computer 49(5), 78–81 (2016)

    CrossRef  Google Scholar 

  13. Zhu, W., Luo, C., Wang, J., Li, S.: Multimedia cloud computing. IEEE Signal Process. Mag. 28(3), 59–69 (2011)

    CrossRef  Google Scholar 

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

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Correspondence to Feroz Zahid .

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Pradhan, D., Zahid, F. (2019). Data Center Clustering for Geographically Distributed Cloud Deployments. In: Barolli, L., Takizawa, M., Xhafa, F., Enokido, T. (eds) Web, Artificial Intelligence and Network Applications. WAINA 2019. Advances in Intelligent Systems and Computing, vol 927. Springer, Cham.

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