  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.
