Research on data center energy consumption optimization scheduling strategy based on reinforcement learning
The rapid growth of cloud computing has exacerbated data center energy consumption issues,necessitating intelligent and efficient optimization methods.This paper analyzes the problem,explores the potential of reinforcement learning in energy scheduling,constructs a comprehensive data center model,formalizes the scheduling problem as a Markov decision process,and proposes a deep reinforcement learning algorithm combining graph neural networks and long short-term memory networks.Simulations verify the effectiveness of the proposed strategy.
data centerenergy optimizationreinforcement learningscheduling
NETL
NSTL
万方数据
