Evolutionary Multiobjective Query Workload Optimization of Cloud Data Warehouses

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Date

2014

Author

Dokeroglu, Tansel
Sert, Seyyit Alper
Cinar, Muhammet Serkan

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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With the advent of Cloud databases, query optimizers need to find paretooptimal solutions in terms of response time and monetary cost. Our novel approach minimizes both objectives by deploying alternative virtual resources and query plans making use of the virtual resource elasticity of the Cloud. We propose an exact multiobjective branch-and-bound and a robust multiobjective genetic algorithm for the optimization of distributed data warehouse query workloads on the Cloud. In order to investigate the effectiveness of our approach, we incorporate the devised algorithms into a prototype system. Finally, through several experiments that we have conducted with different workloads and virtual resource configurations, we conclude remarkable findings of alternative deployments as well as the advantages and disadvantages of the multiobjective algorithms we propose.

Subject Keywords

General Biochemistry, Genetics and Molecular Biology, General Environmental Science, General Medicine

URI

https://hdl.handle.net/11511/51460

Journal

The Scientific World Journal

DOI

https://doi.org/10.1155/2014/435254

Collections

Department of Computer Engineering, Article

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Citation Formats

T. Dokeroglu, S. A. Sert, and M. S. Cinar, “Evolutionary Multiobjective Query Workload Optimization of Cloud Data Warehouses,” The Scientific World Journal, pp. 1–16, 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/51460.