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Resource Auto-scaling for SQL-like Queries in the Cloud based on Parallel Reinforcement Learning

Kandi, Mohamed Mehdi and Yin, Shaoyi and Hameurlain, Abdelkader Resource Auto-scaling for SQL-like Queries in the Cloud based on Parallel Reinforcement Learning. (2019) International Journal of Grid and Utility Computing, 10 (6). 654-671. ISSN 1741-847X

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Official URL: https://dx.doi.org/10.1504/IJGUC.2019.102748


Cloud computing is a technology that provides on-demand services in which the number of assigned resources can be automatically adjusted. A key challenge is how to choose the right number of resources so that the overall monetary cost is minimised. This problem, known as auto-scaling, was addressed in some existing works but most of them are dedicated to web applications. In these applications, it is assumed that the queries are atomic and each of them uses a single resource for a short period of time. However, this assumption cannot be considered for database applications. A query, in this case, contains many dependent and long tasks so several resources are required. We propose in this work an auto-scaling method based on reinforcement learning. The method is coupled with placement-scheduling. In the experimental section, we show the advantage of coupling the auto-scaling to the placement-scheduling by comparing our work to an existing auto-scaling method.

Item Type:Article
Additional Information:https://www.inderscience.com/offer.php?id=102748
HAL Id:hal-02451047
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Laboratory name:
Deposited On:08 Jan 2020 11:11

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