用深度强化学习优化边缘云协同资源调度，提升效率与稳定性。
Research on Edge Computing and Cloud Collaborative Resource Scheduling Optimization Based on Deep Reinforcement Learning
- 基于深度强化学习构建动态调度策略，自适应分配任务
- 实验显示处理时间减少30%以上，资源利用率显著提升
- 适合研究智能边缘计算与分布式系统优化的开发者
本研究针对边缘-云协同计算中的资源调度优化问题，提出一种基于深度强化学习（DRL）的方法。该方法有效提升任务处理效率，降低整体处理时延，改善资源利用率，并控制任务迁移频率。实验结果表明，相较于传统调度算法，DRL在复杂任务分配、动态工作负载及多重资源约束场景下表现更优。尽管如此，仍需进一步提升学习效率、缩短训练时间并解决收敛性问题。未来研究应增强算法在复杂不确定环境下的容错能力，推动边缘-云系统的智能化与高效化发展。
原文摘要 · Abstract (English)
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.
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