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基于BMAP的可编程P4交换机在5G-IoT生态系统中的延迟和缓冲的随机建模
Viacheslav Kovtun1, Maria Yukhimchuk1, Jamil Abedalrahim Jamil Alsayaydeh2
1Department of Computer Control Systems, Faculty of Intelligent Information Technologies and Automation, Vinnytsia National Technical University, Vinnytsia, Ukraine.
PloS one
|August 29, 2025
概括
这项研究介绍了5G-IoT网络的混合随机模型,使用批量马科维亚到达过程 (BMAP) 准确预测延迟和缓冲. 与传统方法相比,BMAP模型显著减少了爆发性物联网流量的错误.
科学领域:
- 计算机科学
- 电气工程
- 网络性能分析
背景情况:
- 由于复杂的批量流量模式, 5G-IoT 生态系统在准确建模流量延迟和缓冲方面面临挑战.
- 现有的排队模型往往无法捕捉物联网流量的爆发性和可编程交换机中控制平面交互的复杂性.
研究的目的:
- 开发和验证一种新的混合随机模型,用于评估5G-IoT网络中的延迟和缓冲.
- 使用先进的队列框架准确地描述时间变化和混合路由逻辑.
- 提高现实世界物联网流量场景的预测准确度.
主要方法:
- 集成批量马科夫到达过程 (BMAP) 与相型服务和半马科夫控制平面建模.
- 使用扩展的G/G/1,H2/H2/1,M/G/1和M/N/1排队框架来导出分析表达式.
- 在真实世界的交通数据集上进行验证,并与Poisson和Markov调制Poisson过程 (MMPP) 模型进行比较.
主要成果:
- BMAP模型表现出卓越的准确性,在爆发性物联网流量时,模型误差降低了高达38% (相对于Poisson) 和22% (相对于MMPP).
- 增加控制平面的参与显著增加了处理延迟,观察到2.6倍的增加.
- H2/H2/1模型表现出最高的经验数据对齐,捕获多相服务和控制流量和.
结论:
- 拟议的混合随机模型,特别是BMAP,提供了比传统模型更准确的5G-IoT流量动态.
- 该研究强调了控制平面交互和流量爆发对网络性能的影响.
- 这些发现为优化5G-IoT网络设计和性能管理提供了宝贵的见解.
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