Frequency Selection Approach for Energy Aware Cloud Database - Guo, Chaopeng; Pierson, Jean-Marc (Anais do International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD); 2018: Anais do XXX International Symposium on Computer Architecture and High Performance Computing, 2018)
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 over-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 over-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 the algorithms have great scalability which can be applied to a 120-nodes case with high accuracy compared to optimal solutions (up to 99.9% and 99.6% for GA and MCTS respectively). According to an optimality bound analysis, 21 % of energy can be saved at most using our frequency selection approach.