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

TL;DR

This paper proposes a deep reinforcement learning approach for optimizing resource scheduling in edge-cloud systems, significantly improving efficiency and resource utilization over traditional methods.

Contribution

It introduces a novel DRL-based scheduling algorithm tailored for edge-cloud environments, addressing complex task allocation and dynamic workloads.

Findings

DRL outperforms traditional scheduling algorithms in efficiency

Enhanced resource utilization and task processing times

Effective management of complex, dynamic workloads

Abstract

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.