Energy Aware Resource Scheduling Algorithm for Data Center Using Reinforcement Learning
Jingling Yuan, Jiang Xing, Luo Zhong, Hui Yu · 2012
More and more attention is paid for energy consumption aware and power control for data center with the emergency of energy crisis. The use of virtualization technology makes it possible for dynamic configuration of data center resources. The N:1 mapping visualization technology is employed to integrate many physical machines into an virtual resource pool to control resources centralized, and then reinforcement learning is applied to resource management and decision making for an uncertain task flow data center. Finally, an automatic resource control algorithm with energy consumption aware is proposed. This algorithm is implemented in the Cloud Sim platform to improve the energy consumption of the data center. The experimental results show that our algorithm can reduce about 40% of the energy consumption of the non-power-aware data center and reduce 1.7% of that of the greedy scheduling algorithm in data center.