Designing cloud data warehouses using multiobjective evolutionary algorithms

Date

2014-01-01

Author

Dökeroǧlu, Tansel
Sert, Seyyit Alper
Çinar, M. Serkan
Coşar, Ahmet

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

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DataBase as a Service (DBaaS) providers need to improve their existing capabilities in data management and balance the efficient usage of virtual resources to multi-users with varying needs. However, there is still no existing method that concerns both with the optimization of the total ownership price and the performance of the queries of a Cloud data warehouse by taking into account the alternative virtual resource allocation and query execution plans. Our proposed method tunes the virtual resources of a Cloud to a data warehouse system, whereas most of the previous studies used to tune the database/queries to a given static resource setting. We solve this important problem with an exact Branch and Bound algorithm and a robust Multiobjective Genetic Algorithm. Finally, through several experiments we conclude remarkable findings of the algorithms we propose.

Subject Keywords

Cloud, Elasticity, Multiobjective data warehouse design, Virtualization

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84902345342&origin=inward
https://hdl.handle.net/11511/95309

DOI

https://doi.org/10.5220/0004906805710576

Conference Name

6th International Conference on Agents and Artificial Intelligence, ICAART 2014

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

T. Dökeroǧlu, S. A. Sert, M. S. Çinar, and A. Coşar, “Designing cloud data warehouses using multiobjective evolutionary algorithms,” Angers, Fransa, 2014, vol. 1, Accessed: 00, 2022. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84902345342&origin=inward.