在MEC工作负载中部署DRL驱动的智能SFC,用于动态物联网网络
Seyha Ros1, Intae Ryoo2, Seokhoon Kim1,3
1Department of Software Convergence, Soonchunhyang University, Asan 31538, Republic of Korea.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
本研究介绍了物联网 (IoT) 任务卸载和资源编排在多访问边缘计算 (MEC) 中的智能框架. 深度增强学习优化了资源配置,减少了延迟和能源消耗.
科学领域:
- 计算机科学 计算机科学
- 网络工程 网络工程
- 人工智能的人工智能
背景情况:
- 物联网 (IoT) 传感器网络的扩散产生了大量的数据,需要有效的资源管理.
- 在异质物联网网络中维持服务质量 (QoS) 受到有限的多访问边缘计算 (MEC) 资源和日益增长的任务卸载需求的挑战.
- 网络拥堵,服务延迟和资源利用效率低下会降低物联网-MEC系统的性能.
研究的目的:
- 为动态的物联网-MEC环境提出一个智能任务卸载和资源编排框架.
- 为了优化能源消耗,计算成本,网络拥堵和服务延迟.
- 提高整体系统效率,并实现边缘计算的最佳政策.
主要方法:
- 开发了一个整合任务卸载和动态资源编排的框架.
- 用户服务功能链 (SFC) 用于虚拟网络功能 (VNF) 的放置和路由路径的确定.
- 利用深度强化学习 (DRL),特别是深度Q网络 (DQN),用于适应性资源配置和任务卸载决策.
主要成果:
- 基于DRL的方案显著超过了参考方法.
- 证明了服务延迟和能源消耗的大幅度减少.
- 展示了交付,吞吐量和累积奖励方面的改进.
结论:
- 拟议的智能框架有效地解决了物联网-MEC环境中的挑战.
- DRL驱动的动态资源编排和任务卸载优化了系统性能.
- 该方法为边缘计算中的资源管理提供了灵活和适应性的解决方案.
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