强化学习下能耗优化的虚拟机放置策略

Virtual Machine Placement Strategy with Energy Consumption Optimization under Reinforcement Learning

云数据中心的高速发展带来了非常强大的计算能力,但是伴随产生的能耗问题也日益严重。为了降低云数据中心内物理服务器的能耗开销,首先利用强化学习对虚拟机放置问题进行建模,随后结合实际问题从状态聚合和时间信度两个方面对Q-Learning(λ)算法进行优化,最后通过云仿真平台CloudSim和实际数据集对虚拟机放置问题进行实验。实验结果表明,与Q-Learning算法、Greedy算法和PSO算法相比,优化后的Q-Learning(λ)算法更有效地降低了物理服务器的能耗开销,同时针对不同数量的虚拟机放置请求也能够保证更好的结果,具有较强的实用价值。

Although the rapid development of cloud data centers has brought very powerful computing power,the energy consumption problem has become increasingly serious.In order to reduce the energy consumption of physical servers in cloud data centers,firstly the virtual machine placement problem is modeled by reinforcement learning.Then,the Q-Learning(λ) algorithm is optimized from two aspects:state aggregation and time reliability.Finally,the virtual machine placement problem is simulated through cloud simulation platform CloudSim and actual data.The simulation results show that the optimized Q-Learning(λ) algorithm can effectively reduce the energy consumption of the cloud data center compared with the Greedy algorithm,PSO algorithm and Q-Learning algorithm,and can ensure better results for different numbers of virtual machine placement requests.The proposed algorithm has strong practical value.