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Frequency Selection Approach for Energy Aware Cloud Database

Guo, Chaopeng and Pierson, Jean-Marc and Hui, Liu and Song, Jie Frequency Selection Approach for Energy Aware Cloud Database. (2018) IEEE Access, 7 (1). 1927-1942. ISSN 2169-3536

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Official URL: https://doi.org/10.1109/ACCESS.2018.2885765

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.

Item Type:Article
HAL Id:hal-02092942
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (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)
Other partners > Northeastern University (CHINA)
Laboratory name:
Funders:
National Natural Science Foundation of China under Grant 61672143, Grant 61662057 and Grant 51606031 - Fundamental Research Funds for the Central Universities under Grant N161602003 - China Scholarship Council under Grant CSC N201506080029
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Deposited On:02 Apr 2019 10:01

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