使用强化学习的边缘物联网中分散的队列控制与延迟转移
1Vinnytsia National Technical University, Vinnytsia, Ukraine. kovtun_v_v@vntu.edu.ua.
Scientific reports
|August 22, 2025
概括
本研究介绍了边缘物联网系统的适应模型,以有效地管理请求服务. 它通过动态调整处理时间来提高服务质量 (QoS) 和能源效率,即使流量不稳定.
科学领域:
- 计算机科学
- 电气工程
- 应用数学
背景情况:
- 边缘物联网系统面临着越来越高的能源效率,响应能力和自我调节的挑战.
- 边缘网络的不稳定交通条件需要适应性服务管理策略.
- 现有的模型往往缺乏灵活性来处理动态质量和能源管理要求.
研究的目的:
- 在边缘物联网系统的外围节点中开发适应式方法来建模和管理请求服务流程.
- 在波动的交通条件下提高能源效率,响应能力和自我调节.
- 为动态质量和能源管理提供可扩展和流量类型无关的解决方案.
主要方法:
- 建议使用参数化时间转移的随机G/G/1模型来解释设备的不可用性.
- 服务质量 (QoS) 指标的分析表达式 (延迟,可变性,损失,能源消耗) 被导出为转移参数的函数.
- 基于深度Q网络 (DQN) 的强化学习代理用于分散的实时控制转移参数.
主要成果:
- 与最先进的模型相比,平均延迟减少了17-26%
- 实现了服务时间的波动减少,并在峰值负载后改善了队列恢复稳定性.
- 拟议的解决方案不依赖于流量类型,并且可以跨多种边缘架构进行扩展.
结论:
- 适应性方法有效地模拟和管理边缘物联网系统中的服务流程,提高质量和能源效率.
- 基于DQN的代理提供动态,分散的控制,适应实时队列状态.
- 这些发现适用于传感器网络,5G/6G边缘场景以及需要动态质量和能源管理的系统.
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