Modern networks increasingly rely on machine learning models for real-time insights, including traffic classification, application quality of experience inference, and intrusion detection. However, existing approaches prioritize prediction accuracy without considering deployment constraints or the dynamism of network traffic, leading to potentially suboptimal performance. Because of this, deploying ML models in real-world networks with tight performance constraints remains an open challenge. In contrast with existing work that aims to select an optimal candidate model for each task based on offline information, we propose an online, system-driven approach to dynamically select the best ML model for network traffic analysis. To this end, we present Cruise Control, a system that pre-trains several models for a given task with different accuracy-cost tradeoffs and selects the most appropriate model based on lightweight signals representing the system's current traffic processing ability. Experimental results using two real-world traffic analysis tasks demonstrate Cruise Control's effectiveness in adapting to changing network conditions. Our evaluation shows that Cruise Control improves median accuracy by 2.78% while reducing packet loss by a factor of four compared to offline-selected models.

翻译：现代网络日益依赖机器学习模型获取实时洞察，包括流量分类、应用体验质量推断和入侵检测。然而，现有方法优先考虑预测准确性，未考虑部署约束或网络流量的动态特性，可能导致性能欠佳。因此，在具有严格性能约束的实际网络中部署机器学习模型仍然是一个开放挑战。与现有基于离线信息为每个任务选择最优候选模型的研究不同，我们提出一种在线、系统驱动的方法，动态选择适用于网络流量分析的最佳机器学习模型。为此，我们提出Cruise Control系统，该系统针对给定任务预训练多个具有不同精度-成本权衡的模型，并根据代表系统当前流量处理能力的轻量级信号选择最合适的模型。基于两个实际流量分析任务的实验结果表明，Cruise Control能有效适应变化的网络条件。评估显示，与离线选择的模型相比，Cruise Control将中位准确率提高了2.78%，同时将丢包率降低了四倍。