Data Centers Selection for Moving Geo-distributed Big Data to Cloud

Jiangtao Zhang,
Qiang Yuan,
Shi Chen,
Hejiao Huang,
Xuan Wang,

Abstract


Because of the distributed networking and coexistent abundant computation and storage resources, cloud computing has become a preferred platform for big data analytics, especially for the geo-distributed data across the world. The precondition for data processing is to move the data to the cloud. Due to the large volume of data, high transmission cost across continents and even specific legal prohibition, it is not always feasible to move all data to one data center. Appropriate data centers should be selected while keeping fast data access and low cost. In this paper, four criteria of the problem are explored. A tight 3-approximation algorithm is proposed to address the former two criteria. It can be simplified when the underlying bipartite graph is complete. The latter two criteria are addressed by a heuristic. Comparing to the optimal method and other schemes, extensive simulations demonstrate that the proposed algorithms can find rather good solutions with less time, and hence are more appropriate for large scale applications.


Citation Format:
Jiangtao Zhang, Qiang Yuan, Shi Chen, Hejiao Huang, Xuan Wang, "Data Centers Selection for Moving Geo-distributed Big Data to Cloud," Journal of Internet Technology, vol. 20, no. 1 , pp. 111-122, Jan. 2019.

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