# Research on Edge Computing and Cloud Collaborative Resource Scheduling Optimization Based on Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2502.18773 · HTML: https://mlsanity.com/paper/2502.18773/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 26 Feb 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 3 of 3)

- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [Deep Reinforcement Learning for Collaborative Edge Computing in Vehicular Networks](https://mlsanity.com/paper/2010.01722/) (2020)
- [Research on Enhancing Cloud Computing Network Security using Artificial Intelligence Algorithms](https://mlsanity.com/paper/2502.17801/) (2025)

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

- [PLM: Efficient Peripheral Language Models Hardware-Co-Designed for Ubiquitous Computing](https://mlsanity.com/paper/2503.12167/) (2025)
- [Cloud Computing Energy Consumption Prediction Based on Kernel Extreme Learning Machine Algorithm Improved by Vector Weighted Average Algorithm](https://mlsanity.com/paper/2503.04088/) (2025)

## Abstract (arXiv)

This study addresses the challenge of resource scheduling optimization in edge-cloud collaborative computing using deep reinforcement learning (DRL). The proposed DRL-based approach improves task processing efficiency, reduces overall processing time, enhances resource utilization, and effectively controls task migrations. Experimental results demonstrate the superiority of DRL over traditional scheduling algorithms, particularly in managing complex task allocation, dynamic workloads, and multiple resource constraints. Despite its advantages, further improvements are needed to enhance learning efficiency, reduce training time, and address convergence issues. Future research should focus on increasing the algorithm's fault tolerance to handle more complex and uncertain scheduling scenarios, thereby advancing the intelligence and efficiency of edge-cloud computing systems.