可解释的基于学习的时限优化,用于SDN中准确有效的象流预测
Ling Xia Liao1, Changqing Zhao1, Roy Xiaorong Lai2
1School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin 541004, China.
Sensors (Basel, Switzerland)
|February 10, 2024
概括
本研究引入了一种新的方法,用于使用采样流量数据预测软件定义网络 (SDN) 中的高带宽象流. 这种方法显著减少了网络开销,并提高了预测准确度,即使使用不完整的流量信息.
科学领域:
- 计算机科学 计算机科学
- 网络工程 网络工程
背景情况:
- 软件定义网络 (SDN) 需要高效的流量预测以获得最佳性能.
- 目前使用完整流量数据的方法会带来大量的带宽和延迟成本.
研究的目的:
- 使用不完整的交通数据,制定大象流动的预测策略.
- 为了减少SDN中的控制通道开销和网络延迟.
主要方法:
- 实施流入时限策略,具有初始硬时限 (Tinitial) 和增长率 (r).
- 使用后勤回归用于大象流模型和贝叶斯优化用于调整Tinitial和r.
- 采用特征选择,模型学习和对采样流量数据的优化.
主要成果:
- 在各种数据集 (校园,骨干,物联网) 中实现了超过90%的概括精度.
- 成功预测大象的流动大约50%的寿命.
- 对校园和物联网网络的控制器-开关交互显著减少.
结论:
- 提出的基于不完整流量的预测策略对于优化SDN是有效的.
- 这种方法提供了一种可行的解决方案,可以减少网络开销,同时保持高预测准确度.
- 在具有非常短的数据包到达时间的网络中,可能需要对数据包完成进行进一步考虑.
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