用预测驱动的探针规划，让高负载交换机监控更精准、开销减半
NTP-INT: Network Traffic Prediction-Driven In-band Network Telemetry for High-load Switches
- 先预测流量识别高负载交换机，再剪枝生成子网
- 在子网中用深度强化学习规划探针路径，提升效率
- 实验显示精度提升且控制开销降低50%
带内网络遥测（INT）因具备实时可视性，在网络管理中至关重要。然而，随着网络设备和服务快速增长，动态环境中对关键网络信息的定向访问变得尤为关键。本文提出一种智能网络遥测系统NTP-INT，用于在高负载交换机上获取更细粒度的网络信息。NTP-INT包含三个模块：网络流量预测模块、网络剪枝模块和探针路径规划模块。首先，流量预测模块采用多时序图神经网络（MTGNN）预测未来流量并识别高负载交换机；随后，设计网络剪枝算法生成覆盖所有高负载交换机的子网，以降低探针路径规划复杂度；最后，探针路径规划模块使用基于注意力机制的深度强化学习（DEL）模型，在网络切片中规划高效探针路径。实验结果表明，NTP-INT可在高负载交换机上获取更精确的网络信息，同时将控制开销降低50%。
原文摘要 · Abstract (English)
In-band network telemetry (INT) is essential to network management due to its real-time visibility. However, because of the rapid increase in network devices and services, it has become crucial to have targeted access to detailed network information in a dynamic network environment. This paper proposes an intelligent network telemetry system called NTP-INT to obtain more fine-grained network information on high-load switches. Specifically, NTP-INT consists of three modules: network traffic prediction module, network pruning module, and probe path planning module. Firstly, the network traffic prediction module adopts a Multi-Temporal Graph Neural Network (MTGNN) to predict future network traffic and identify high-load switches. Then, we design the network pruning algorithm to generate a subnetwork covering all high-load switches to reduce the complexity of probe path planning. Finally, the probe path planning module uses an attention-mechanism-based deep reinforcement learning (DEL) model to plan efficient probe paths in the network slice. The experimental results demonstrate that NTP-INT can acquire more precise network information on high-load switches while decreasing the control overhead by 50\%.
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