Monitoring Flows with Per-Application Granularity using Programmable Data Planes
Rhaban Hark, Mohamed Ghanmi, Ralf Kundel, Patrick Lieser, Ralf Steinmetz · 2021
The accurate and timely knowledge of a network's internal state is essential for various network management operations like routing, resource allocation, or even intrusion detection. This especially holds true for highly flexible, programmable networks that quickly react to dynamic conditions. However, current approaches of state monitoring in such networks rely on per-rule counter information. Due to limited rule space, their granularity is strongly limited. This generally yields an aggregated and therefore altered representation of the network state. Utilizing the programmability of today's data planes, we tackle this problem and present a novel approach to increase the measurement granularity up to per-application statistics. For demonstration purposes, we show how our approach greatly improves the estimation of the Flow Size Distribution.