# Online Learning for Offloading and Autoscaling in Renewable-Powered Mobile Edge Computing

arXiv: https://arxiv.org/abs/1609.05087 · HTML: https://mlsanity.com/paper/1609.05087/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2016.

## Built on by (most cited first, 2 of 2)

- [Online Learning for Orchestration of Inference in Multi-User End-Edge-Cloud Networks](https://mlsanity.com/paper/2202.10541/) (2022)
- [Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence](https://mlsanity.com/paper/2201.11410/) (2022)

## Abstract (arXiv)

Mobile edge computing (a.k.a. fog computing) has recently emerged to enable in-situ processing of delay-sensitive applications at the edge of mobile networks. Providing grid power supply in support of mobile edge computing, however, is costly and even infeasible (in certain rugged or under-developed areas), thus mandating on-site renewable energy as a major or even sole power supply in increasingly many scenarios. Nonetheless, the high intermittency and unpredictability of renewable energy make it very challenging to deliver a high quality of service to users in renewable-powered mobile edge computing systems. In this paper, we address the challenge of incorporating renewables into mobile edge computing and propose an efficient reinforcement learning-based resource management algorithm, which learns on-the-fly the optimal policy of dynamic workload offloading (to centralized cloud) and edge server provisioning to minimize the long-term system cost (including both service delay and operational cost). Our online learning algorithm uses a decomposition of the (offline) value iteration and (online) reinforcement learning, thus achieving a significant improvement of learning rate and run-time performance when compared to standard reinforcement learning algorithms such as Q-learning.