Big data energy efficiency strategy model in cloud environment based on reinforcement learning
Big data requires a large amount of cloud resources for data processing and analysis,which consumes a lot of energy to run.In the cloud environment where big data is processed,the number of resources and tasks increases exponentially,leading to an increase of power consumption in cloud data centers.Based on this,a reinforcement learning based big data energy efficiency strategy model in cloud environment is proposed,in which the integration of DPSO and DQN is utilized to better estimate and correct data dimensionality defects.The proposed model was compared with traditional DQN and load sensing algorithms.The results indicate that as the number of tasks increases,the proposed model outperforms traditional DQN and load sensing algorithms in big data processing,providing an energy-saving schedule for resource allocation in green cloud environments.
万方数据
维普
