Journal
IEEE ACCESS
Volume 8, Issue -, Pages 178526-178538Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.3027675
Keywords
Data centers; Big Data; Data models; Task analysis; Data analysis; Distributed databases; Companies; Big data analysis; cloud data centers; distributed computing; random sample partition; wide area analytics
Categories
Funding
- National Natural Science Foundation of China [61972261]
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As the volume of data grows rapidly, storing big data in a single data center is no longer feasible. Hence, companies have developed two scenarios to store their big data in multiple data centers. In the first scenario, the company's big data are distributed in multiple data centers without data replication. In the second scenario, data are also stored in multiple data centers but important data are replicated in these data centers to increase data safety and availability. However, in these scenarios, analyzing big data distributed in multiple data centers becomes a challenging task. In this paper, we propose two data distribution strategies to support big data analysis across geo-distributed data centers. In these strategies, we use the recent Random Sample Partition data model to convert big data into sets of random sample data blocks and distribute these data blocks into multiple data centers either without replication or with replication. In analyzing big data in multiple data centers without replication, we randomly select samples of data blocks from multiple data centers and download the sample data blocks to one data center for analysis. In the second strategy with replication of data blocks, we can analyze big data on any data center by randomly selecting a sample of data blocks replicated from other data centers. This strategy avoids data transformation between data centers. We demonstrate the performance of the two strategies in big data analysis by using simulation results produced on one local data center and four AWS data centers in North America, Asia, and Australia.
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