通过强化学习实现智能边缘计算,以公共边缘的卸载作为服务为基础
Ateeqa Jalal1, Umar Farooq1,2,3, Ihsan Rabbi1,3
1Institute of Computing Science and Information Technology (ICSIT), University of Science and Technology, Bannu (UST Bannu), Bannu, Pakistan.
Scientific reports
|February 1, 2026
概括
公共边缘即服务 (PEaaS) 通过使用近接政策优化 (PPO) 调度来优化物联网 (IoT) 卸载. 这种方法有效地管理资源,减少移动物联网大数据处理的延迟和成本.
科学领域:
- 计算机科学 计算机科学
- 分布式系统 分布式系统
- 人工智能的人工智能
背景情况:
- 物联网 (IoT) 部署面临着延迟,成本和资源利用方面的挑战.
- 传统的卸载策略往往忽视中间层和设备移动性,导致效率低下.
- 需要先进的框架来优化动态物联网环境中的资源配置.
研究的目的:
- 提出和评估公共边缘作为服务 (PEaaS) 作为物联网卸载的中间层.
- 开发一个模拟框架,RegionalEdgeSimPy,用于建模和评估PEaaS架构.
- 为了优化任务卸载决策,考虑延迟,成本,拥堵和能源.
主要方法:
- 开发了RegionalEdgeSimPy,这是一个用于PEaaS框架的Python模拟器.
- 实施了一个近接政策优化 (PPO) 调度器,包括移动性和多个输入参数.
- 使用动作掩护和多目标奖励功能,用于智能卸载决策.
- 在智慧城市环境中对10到3000个设备进行模拟.
主要成果:
- PPO调度优先考虑本地边缘处理,直到过度利用,然后将任务引导到PEaaS.
- PEaaS有效地处理卸载的工作负载,而云资源的使用量最小.
- 平均使用情况:边缘 (75.8%),PEaaS (52.9%),云 (<1.2%).
结论:
- 基于PPO的PEaaS框架大大减少了物联网部署中的延迟,成本和任务失败.
- 拟议的系统证明了在物联网大数据处理中的移动性处理的改进可扩展性.
- PEaaS为分布式物联网资源管理提供了一个高效的中间层.
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