Designing cloud data warehouses using multiobjective evolutionary algorithms

2014-01-01
Dökeroǧlu, Tansel
Sert, Seyyit Alper
Çinar, M. Serkan
Coşar, Ahmet
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.
6th International Conference on Agents and Artificial Intelligence, ICAART 2014

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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.