Learning-Enabled Adaptive Power Capping Scheme for Cloud Data Centers

TL;DR

This paper introduces an adaptive, learning-based power capping scheme for cloud data centers that dynamically adjusts energy limits based on environment perception, improving energy management amid uncertainty and market signals.

Contribution

It develops a novel uncertainty-aware model-based reinforcement learning framework for adaptive power capping in cloud data centers, addressing real-world uncertainties and dynamic conditions.

Findings

Effective energy management demonstrated on Alibaba traces

Improved adaptability to changing environments

Theoretical bounds on decision optimality

Abstract

The rapid growth of the digital economy and artificial intelligence has transformed cloud data centers into essential infrastructure with substantial energy consumption and carbon emission, necessitating effective energy management. However, existing methods face challenges such as incomplete information, uncertain parameters, and dynamic environments, which hinder their real-world implementation. This paper proposes an adaptive power capping framework tailored to cloud data centers. By dynamically setting the energy consumption upper bound, the power load of data centers can be reshaped to align with the electricity price or other market signals. To this end, we formulate the power capping problem as a partially observable Markov decision process. Subsequently, we develop an uncertainty-aware model-based reinforcement learning (MBRL) method to perceive the cloud data center operational environment and optimize power-capping decisions. By incorporating a two-stage uncertainty-aware optimization algorithm into the MBRL, we improve its adaptability to the ever-changing environment. Additionally, we derive the optimality gap of the proposed scheme under finite iterations, ensuring effective decisions under complex and uncertain scenarios. The numerical experiments validate the effectiveness of the proposed method using a cloud data center operational environment simulator built on real-world production traces from Alibaba, which demonstrates its potential as an efficient energy management solution for cloud data centers.