在物联网骨干网络中的流量管理使用GNN和MAB与SDN编排
Yanmin Guo1, Yu Wang1, Faheem Khan2
1Shandong Research Institute of Industrial Technology, Jinan 250061, China.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
本研究介绍了一种用于使用图形神经网络和多臂盗的软件定义物联网网络的新型流量管理系统. 该方法动态优化了改善网络性能和异常检测的策略.
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:
- 软件定义物联网网络 (SDN-IoTs) 中的传统流量管理与动态流量模式作斗争.
- 排队理论和静态政策不足以确保服务质量 (QoS).
研究的目的:
- 为SDN-IoT开发一个动态的流量管理方法.
- 为了利用图形神经网络 (GNN) 和多臂强盗算法来优化政策.
主要方法:
- 利用GNN来学习和预测实时网络流量模式.
- 采用多臂盗算法来动态优化基于GNN预测的交通管理策略.
- 评估了各种数据集的方法,包括1999年的KDD杯,CAIDA和NSL-KDD.
主要成果:
- 与最先进的方法相比,拟议的方法显示出更高的性能.
- 在吞吐量方面取得了显著的改进,减少了数据包丢失,并减少了网络延迟.
- 有效地识别了网络内的异常流量模式.
结论:
- GNN和多臂盗方法为SDN-IoT中的动态流量管理提供了强大的解决方案.
- 能够有效地分配网络资源,并确保最终用户的服务质量.
- 为增强网络稳定性和安全性提供了一个有希望的方向.
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