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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 servers 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 article, propose neural network-based adaptive frequency adjustment (NAFA), an adaptive processor frequency adjustment solution, to enable an effective plan of the server&#x2019;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&#x2019;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&#x0025; increase in the average request acceptance ratio and up to 50&#x0025; reduction in average request processing time.

Adaptive Processor Frequency Adjustment for Mobile-Edge Computing With Intermittent Energy Supply