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

Abstract : 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.
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Mohamed Mehdi Kandi, Shaoyi Yin, Abdelkader Hameurlain. Resource Auto-scaling for SQL-like Queries in the Cloud based on Parallel Reinforcement Learning. International Journal of Grid and Utility Computing, Inderscience, 2019, 10 (6), pp.654-671. ⟨10.1504/IJGUC.2019.102748⟩. ⟨hal-02451047⟩

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