Towards a Robust and Scalable TCP Flavors Prediction Model from Passive Traffic
Towards a Robust and Scalable TCP Flavors Prediction Model from Passive Traffic
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DOI：
10.1109/icccn.2018.8487396
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
D. Hagos;P. Engelstad;A. Yazidi;Ø. Kure
中科院分区：
文献类型：
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作者：
D. Hagos;P. Engelstad;A. Yazidi;Ø. Kure
广泛使用的不同端到端传输控制协议（TCP）算法在网络拥塞情况下的表现不同。 TCP 拥塞控制本身变得越来越复杂，这在实践中使得通过被动测量来预测 TCP 每个连接状态成为一项具有挑战性的任务。在本文中，我们提出了一种稳健、可扩展且通用的基于机器学习的模型，该模型可能会引起网络运营商的兴趣，该模型通过在中间节点收集的被动流量测量结果实验性地推断出流中基于丢失的 TCP 算法的底层变体。我们相信，我们的研究对于学术界和工业界网络界的研究人员和科学家来说也有潜在的好处和机会，他们想要评估与网络拥塞相关的 TCP 传输状态的特征。我们通过几个受控实验验证了预测模型的稳健性和可扩展性。令人惊讶的是，事实证明，当将学习的预测模型应用于与机器学习社区中的迁移学习概念相似的现实场景设置时，通过利用来自模拟网络的知识，学习的预测模型表现得相当好。我们的实验结果在模拟网络、现实和组合场景设置以及跨多个 TCP 变体中的准确性表明我们的模型是有效的并且具有巨大的潜力。
Different end-to-end Transmission Control Protocol (TCP) algorithms widely in use behave differently under network congestion. The TCP congestion control itself has grown increasingly complex which in practice makes predicting TCP per-connection states from passive measurements a challenging task. In this paper, we present a robust, scalable and generic machine learning-based model which may be of interest for network operators that experimentally infers the underlying variant of loss-based TCP algorithms within a flow from passive traffic measurements collected at an intermediate node. We believe that our study has also a potential benefit and opportunity for researchers and scientists in the networking community from both academia and industry who want to assess the characteristics of TCP transmission states related to network congestion. We validate the robustness and scalability approach of our prediction model through several controlled experiments. It turns out, surprisingly enough, that the learned prediction model performs reasonably well by leveraging knowledge from the emulated network when it is applied on a real-life scenario setting bearing similarity to the concept of transfer learning in the machine learning community. The accuracy of our experimental results both in an emulated network, realistic and combined scenario settings and across multiple TCP variants demonstrate that our model is effective and has considerable potential.