RARE: Renewable Energy Aware Resource Management in Datacenters

TL;DR

RARE is a deep reinforcement learning-based scheduler designed to optimize renewable energy use in datacenters, adapting dynamically to intermittent power supply and outperforming traditional heuristic methods.

Contribution

The paper introduces RARE, a novel DRL-based job scheduler that automatically learns and adapts to complex green datacenter environments, improving renewable energy utilization.

Findings

DRL scheduler outperforms heuristic policies across workloads

Proper tuning of system parameters enhances performance

Offline learning enables improvement over existing heuristics

Abstract

The exponential growth in demand for digital services drives massive datacenter energy consumption and negative environmental impacts. Promoting sustainable solutions to pressing energy and digital infrastructure challenges is crucial. Several hyperscale cloud providers have announced plans to power their datacenters using renewable energy. However, integrating renewables to power the datacenters is challenging because the power generation is intermittent, necessitating approaches to tackle power supply variability. Hand engineering domain-specific heuristics-based schedulers to meet specific objective functions in such complex dynamic green datacenter environments is time-consuming, expensive, and requires extensive tuning by domain experts. The green datacenters need smart systems and system software to employ multiple renewable energy sources (wind and solar) by intelligently adapting computing to renewable energy generation. We present RARE (Renewable energy Aware REsource management), a Deep Reinforcement Learning (DRL) job scheduler that automatically learns effective job scheduling policies while continually adapting to datacenters' complex dynamic environment. The resulting DRL scheduler performs better than heuristic scheduling policies with different workloads and adapts to the intermittent power supply from renewables. We demonstrate DRL scheduler system design parameters that, when tuned correctly, produce better performance. Finally, we demonstrate that the DRL scheduler can learn from and improve upon existing heuristic policies using Offline Learning.