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2014 IEEE 30th International Conference on Data Engineering (ICDE) (2014)
Chicago, IL, USA
March 31, 2014 to April 4, 2014
ISBN: 978-1-4799-2555-1
pp: 1274-1277
Yu Cao , EMC Labs, USA
Xiaoyan Guo , EMC Labs, USA
Baoyao Zhou , EMC Labs, USA
Stephen Todd , EMC Labs, USA
ABSTRACT
This paper demonstrates HOPE, an efficient and effective database partitioning system that is designed for OLTP workloads. HOPE is built on top of a novel tuple-group based database partitioning model, which is able to minimize the number of distributed transactions as well as the extent of partition and workload skews during the workload execution. HOPE conducts the partitioning in an iterative manner in order to achieve better partitioning quality, save the user's time spent on partitioning design and increase its application scenes. HOPE is also highly interactive, as it provides rich opportunities for the user to help it further improve the partitioning quality by passing expertise and indirect domain knowledge.
INDEX TERMS
Database systems, Throughput, Distributed databases, Scalability, Computer architecture, Partitioning algorithms
CITATION

Y. Cao, X. Guo, B. Zhou and S. Todd, "HOPE: Iterative and interactive database partitioning for OLTP workloads," 2014 IEEE 30th International Conference on Data Engineering (ICDE), Chicago, IL, USA, 2014, pp. 1274-1277.
doi:10.1109/ICDE.2014.6816759
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