论文标题
流量分析开发套件（TADK）：在网络应用中启用实时AI推断
Traffic Analytics Development Kits (TADK): Enable Real-Time AI Inference in Networking Apps
论文作者
论文摘要
复杂的流量分析，例如加密的流量分析和未知的恶意软件检测，强调了对分析网络流量的高级方法的需求。使用固定模式，签名匹配和规则来检测网络流量中已知模式的传统方法正在用AI（人工智能）驱动算法代替。但是，没有高性能AI网络特定的框架使得不可能在网络工作负载中部署基于AI的处理。在本文中，我们描述了流量分析开发套件（TADK）的设计，这是一个针对基于AI的网络工作负载处理的行业标准框架。 TADK可以在不需要专门的硬件（例如GPU，神经处理单元等）的情况下，从数据中心到边缘的网络设备中基于实时的网络工作负载处理。我们已经在商品WAF和5G UPF中部署了TADK，评估结果表明，TADK可以在流量功能提取时达到每核35.3Gbps的吞吐量，每核6.5Gbps在流量分类中，可以减少SQLI/XSS检测到与固定图案更高的速度相比，每个请求下降到4.5US。
Sophisticated traffic analytics, such as the encrypted traffic analytics and unknown malware detection, emphasizes the need for advanced methods to analyze the network traffic. Traditional methods of using fixed patterns, signature matching, and rules to detect known patterns in network traffic are being replaced with AI (Artificial Intelligence) driven algorithms. However, the absence of a high-performance AI networking-specific framework makes deploying real-time AI-based processing within networking workloads impossible. In this paper, we describe the design of Traffic Analytics Development Kits (TADK), an industry-standard framework specific for AI-based networking workloads processing. TADK can provide real-time AI-based networking workload processing in networking equipment from the data center out to the edge without the need for specialized hardware (e.g., GPUs, Neural Processing Unit, and so on). We have deployed TADK in commodity WAF and 5G UPF, and the evaluation result shows that TADK can achieve a throughput up to 35.3Gbps per core on traffic feature extraction, 6.5Gbps per core on traffic classification, and can decrease SQLi/XSS detection down to 4.5us per request with higher accuracy than fixed pattern solution.
