Adaptive Processor Frequency Adjustment for Mobile Edge Computing with Intermittent Energy Supply

TL;DR

This paper introduces NAFA, a deep reinforcement learning-based adaptive processor frequency adjustment method for renewable-energy-powered MEC servers, improving energy efficiency and service quality amidst intermittent energy supply.

Contribution

It presents a novel deep reinforcement learning approach for dynamic processor frequency adjustment in MEC servers with renewable energy, enhancing sustainability and performance.

Findings

Up to 20% increase in request acceptance ratio

Up to 50% reduction in request processing time

Effective energy management balancing sustainability and quality

Abstract

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.