MonPlan: Taming Network Measurement with Accurate and Resource-Efficient Sketch-INT Co-Design

摘要

Network measurement is now a basic building block that supports network management applications to identify real-time events by measuring both large and small flows. However, existing techniques face the trade-off between high accuracy and resource efficiency: sketch-based techniques enjoy high accuracy and low resource consumption when measuring large flows but drop accuracy for small flows; in-band network telemetry (INT) measures every flow at the cost of high resource consumption. In this paper, we present MonPlan, a framework that utilizes both sketches and INT to measure large and small flows, respectively, offering both high measurement accuracy and resource efficiency. Unlike existing studies, our co-design is driven by two theoretical optimizations: (1) MonPlan adopts the near-optimal Lagrangian relaxation to solve the NP-hard selection of measurement points to deploy sketches and INT even when the routing information is unknown. (2) MonPlan estimates the worst-case rates of collecting measurement data. Its estimates support reinforcement learning to select paths without network congestions. We realize MonPlan on 12.8 Tbps programmable switches. With large-scale real-world topologies and nine network management applications, MonPlan provides 23∼95% higher accuracy and retains resource efficiency when compared with five types of existing techniques.