# dRG-MEC: Decentralized Reinforced Green Offloading for MEC-enabled Cloud Network

arXiv: https://arxiv.org/abs/2402.00874 · HTML: https://mlsanity.com/paper/2402.00874/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 10 Jan 2024.

## Builds on (most cited first, 7 of 7)

- [A Distributional Perspective on Reinforcement Learning](https://mlsanity.com/paper/1707.06887/) (2017)
- [Learning-Based Computation Offloading for IoT Devices with Energy Harvesting](https://mlsanity.com/paper/1712.08768/) (2017)
- [Actions at the Edge: Jointly Optimizing the Resources in Multi-access Edge Computing](https://mlsanity.com/paper/2204.08169/) (2022)
- [Low-latency Networking: Where Latency Lurks and How to Tame It](https://mlsanity.com/paper/1808.02079/) (2018)
- [A Quantum Approach Towards the Adaptive Prediction of Cloud Workloads](https://mlsanity.com/paper/2211.14619/) (2022)
- [Efficient 3D Aerial Base Station Placement Considering Users Mobility by Reinforcement Learning](https://mlsanity.com/paper/1801.07472/) (2018)
- [Holo-Block Chain: A Hybrid Approach for Secured IoT Healthcare Ecosystem](https://mlsanity.com/paper/2304.14175/) (2023)

## Abstract (arXiv)

Multi-access-Mobile Edge Computing (MEC) is a promising solution for computationally demanding rigorous applications, that can meet 6G network service requirements. However, edge servers incur high computation costs during task processing. In this paper, we proposed a technique to minimize the total computation and communication overhead for optimal resource utilization with joint computational offloading that enables a green environment. Our optimization problem is NP-hard; thus, we proposed a decentralized Reinforcement Learning (dRL) approach where we eliminate the problem of dimensionality and over-estimation of the value functions. Compared to baseline schemes our technique achieves a 37.03% reduction in total system costs.