|Authors||D. Pradhan and F. Zahid|
|Title||Data Center Clustering for Geographically Distributed Cloud Deployments|
|Project(s)||MELODIC: Multi-cloud Execution-ware for Large-scale Optimised Data-Intensive Computing|
|Publication Type||Proceedings, refereed|
|Year of Publication||2019|
|Conference Name||International Workshop on Recent Advances for Multi-Clouds and Mobile Edge Computing (M²EC 2019) in conjunction with the 33rd International Conference on Advanced Information Networking and Applications (AINA)|
|Place Published||Matsue, Japan|
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 of 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 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 clusters, show that the proposed clustering schemes are very effective in optimizing data center selection as per the application requirements.