论文标题
探索机器学习，以分类卫星的Quic流量
Exploring Machine Learning for Classification of QUIC Flows over Satellite
论文作者
论文摘要
由于当前加密传输信息的趋势（例如，在HTTP加密隧道之后），自动交通分类越来越重要，该趋势阻止了中间节点以访问端到端的运输标头。本文提出了一种基于自动流量分类的混合陆地和SATCOM网络中支持服务质量（QO）的体系结构。流量配置文件是使用机器学习（ML）算法构建的，使用一系列数据包大小和Quic连接的到达时间。因此，提出的QoS方法不需要路径的明确设置（即它提供了软QoS），而是雇用网络中的代理来验证符合给定的流量配置文件的流动。在一系列ML模型中的结果鼓励将ML技术集成在SATCOM设备中。低成本的较高计算能力的可用性为实施这些技术创造了肥沃的基础。
Automatic traffic classification is increasingly important in networking due to the current trend of encrypting transport information (e.g., behind HTTP encrypted tunnels) which prevents intermediate nodes to access end-to-end transport headers. This paper proposes an architecture for supporting Quality of Service (QoS) in hybrid terrestrial and SATCOM networks based on automated traffic classification. Traffic profiles are constructed by machine-learning (ML) algorithms using the series of packet sizes and arrival times of QUIC connections. Thus, the proposed QoS method does not require an explicit setup of a path (i.e. it provides soft QoS), but employs agents within the network to verify that flows conform to a given traffic profile. Results over a range of ML models encourage integrating ML technology in SATCOM equipment. The availability of higher computation power at a low cost creates fertile ground for the implementation of these techniques.
