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

arXiv: https://arxiv.org/abs/2308.08069 · HTML: https://mlsanity.com/paper/2308.08069/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 34 references that are in the map.
- First posted on arXiv 15 Aug 2023.

## Builds on (most cited first, 3 of 3)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Sustaining Performance While Reducing Energy Consumption: A Control Theory Approach](https://mlsanity.com/paper/2107.02426/) (2021)

## Built on by (most cited first, 1 of 1)

- [Offline Reinforcement-Learning-Based Power Control for Application-Agnostic Energy Efficiency](https://mlsanity.com/paper/2601.11352/) (2026)

## Abstract (arXiv)

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.