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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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.
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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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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. https://doi.org/10.1007/978-3-030-15035-8_101
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