A Reinforcement Learning Approach for Performance-aware Reduction in
  Power Consumption of Data Center Compute Nodes
Akhilesh Raj Swann Perarnau Aniruddha Gokhale
As Exascale computing becomes a reality, the energy needs of compute nodes in
cloud data centers will continue to grow. A common approach to reducing this
energy demand is to limit the power consumption of hardware components when
workloads are experiencing bottlenecks elsewhere in the system. However,
designing a resource controller capable of detecting and limiting power
consumption on-the-fly is a complex issue and can also adversely impact
application performance. In this paper, we explore the use of Reinforcement
Learning (RL) to design a power capping policy on cloud compute nodes using
observations on current power consumption and instantaneous application
performance (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 supplied
power to processors without compromising on application performance. Employing
a Proximal Policy Optimization (PPO) agent to learn an optimal policy on a
mathematical model of the compute nodes, we demonstrate and evaluate using the
STREAM benchmark how a trained agent running on actual hardware can take
actions by balancing power consumption and application performance.