基于深度学习的数字双胞胎网络流量预测方法
Junyu Lai1, Zhiyong Chen1, Junhong Zhu1
1School of Aeronautics & Astronautics, University of Electronic Science and Technology of China, Chengdu, 611731 China.
Cognitive computation
|June 26, 2023
概括
本研究介绍了一种增强的ConvLSTM模型,用于在局域网中准确预测网络流量. 该模型显著提高了数字双胞胎网络的预测准确性,有助于流量同步.
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:
- 网络流量预测 (NTP) 对于网络管理和资源分配至关重要.
- 数字双胞胎网络 (DTNs) 需要准确的流量同步,以实现有效的模拟和仿真.
- 现有的NTP方法在预测复杂的交通模式方面存在局限性.
研究的目的:
- 为局域网 (LAN) 提出一个准确的NTP方法,以支持DTNs中的流量同步.
- 增强深度学习模型,以改善交通矩阵 (TM) 预测.
- 根据基线方法对拟议模型的性能进行评估.
主要方法:
- 对现有的DTN,传统和基于深度学习的NTP方法的调查.
- 开发了一种线性特征增强的卷积长短期记忆 (ConvLSTM) 模型.
- 整合一个自回归单元用于线性预测增强.
- 使用交通模式注意力 (TPA) 和挤压和刺激 (SE) 块进行优化,创建eConvLSTM模型.
主要成果:
- 在NTP准确性方面,eConvLSTM模型显著优于基线方法.
- 与传统的ConvLSTM相比,降低了高达10.6% (单跳) 和16.8% (多跳) 的平均平方误差 (MSE).
- 对eConvLSTM的进一步改进使MSE减少了额外的2.1% (单跳) 和4.2% (多跳).
- 该模型满足实际应用的效率要求.
结论:
- 拟议的eConvLSTM模型为LANs提供了一种优越的NTP方法.
- 这种方法对于在DTN中实现准确的流量同步至关重要.
- 该模型为网络资源管理和模拟提供了强大而高效的解决方案.
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