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As Exascale computing becomes a reality, the energy needs of compute nodes incloud data centers will continue to grow. A common approach to reducing thisenergy demand is to limit the power consumption of hardware components whenworkloads are experiencing bottlenecks elsewhere in the system. However,designing a resource controller capable of detecting and limiting powerconsumption on-the-fly is a complex issue and can also adversely impactapplication performance. In this paper, we explore the use of ReinforcementLearning (RL) to design a power capping policy on cloud compute nodes usingobservations on current power consumption and instantaneous applicationperformance (heartbeats). By leveraging the Argo Node Resource Management (NRM)software stack in conjunction with the Intel Running Average Power Limit (RAPL)hardware control mechanism, we design an agent to control the maximum suppliedpower to processors without compromising on application performance. Employinga Proximal Policy Optimization (PPO) agent to learn an optimal policy on amathematical model of the compute nodes, we demonstrate and evaluate using theSTREAM benchmark how a trained agent running on actual hardware can takeactions by balancing power consumption and application performance.

A Reinforcement Learning Approach for Performance-aware Reduction in Power Consumption of Data Center Compute Nodes