  With astonishing speed, bandwidth, and scale, Mobile Edge Computing (MEC) has
played an increasingly important role in the next generation of connectivity
and service delivery. Yet, along with the massive deployment of MEC servers,
the ensuing energy issue is now on an increasingly urgent agenda. In the
current context, the large scale deployment of renewable-energy-supplied MEC
servers is perhaps the most promising solution for the incoming energy issue.
Nonetheless, as a result of the intermittent nature of their power sources,
these special design MEC server must be more cautious about their energy usage,
in a bid to maintain their service sustainability as well as service standard.
Targeting optimization on a single-server MEC scenario, we in this paper
propose NAFA, an adaptive processor frequency adjustment solution, to enable an
effective plan of the server's energy usage. By learning from the historical
data revealing request arrival and energy harvest pattern, the deep
reinforcement learning-based solution is capable of making intelligent
schedules on the server's processor frequency, so as to strike a good balance
between service sustainability and service quality. The superior performance of
NAFA is substantiated by real-data-based experiments, wherein NAFA demonstrates
up to 20% increase in average request acceptance ratio and up to 50% reduction
in average request processing time.
