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Frequency Selection Approach for Energy Aware Cloud Database

Abstract : A lot of cloud systems are adopted in industry and academia to face the explosion of the data volume and the arrival of the big data era. Meanwhile, energy efficiency and energy saving become major concerns for data centers where massive cloud systems are deployed. However, energy waste is quite common due to resource provisioning. In this paper, using Dynamic Voltage and Frequency Scaling (DVFS), a frequency selection approach is introduced to improve the energy efficiency of cloud systems in terms of resource provisioning. In the approach, two algorithms, Genetic Algorithm (GA) and Monte Carlo Tree Search Algorithm (MCTS), are proposed. Cloud database system is taken as an example to evaluate the approach. The results of the experiments show that both algorithms have its advantages. The algorithms have great scalability, in which GA can be applied to thousands of nodes and MCTS can be applied to hundreds of nodes. Both algorithms have high accuracy compared to optimal solutions (up to 99.9% and 99.6% for GA and MCTS respectively). According to an optimality bound analysis, 26% of energy can be saved at most using our frequency selection approach.
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https://hal.archives-ouvertes.fr/hal-02092942
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Submitted on : Monday, April 8, 2019 - 3:27:11 PM
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Chaopeng Guo, Jean-Marc Pierson, Liu Hui, Jie Song. Frequency Selection Approach for Energy Aware Cloud Database. IEEE Access, IEEE, 2018, 7 (1), pp.1927-1942. ⟨10.1109/ACCESS.2018.2885765⟩. ⟨hal-02092942⟩

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