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

arXiv: https://arxiv.org/abs/2102.05449 · HTML: https://mlsanity.com/paper/2102.05449/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 10 Feb 2021.

## Builds on (most cited first, 6 of 6)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [DMRO:A Deep Meta Reinforcement Learning-based Task Offloading Framework for Edge-Cloud Computing](https://mlsanity.com/paper/2008.09930/) (2020)
- [Online Learning for Offloading and Autoscaling in Energy Harvesting Mobile Edge Computing](https://mlsanity.com/paper/1703.06060/) (2017)
- [Performance Optimization in Mobile-Edge Computing via Deep Reinforcement Learning](https://mlsanity.com/paper/1804.00514/) (2018)
- [A Machine Learning Pipeline Stage for Adaptive Frequency Adjustment](https://mlsanity.com/paper/2007.01820/) (2020)

## Abstract (arXiv)

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.