Monitoring Flows with Per-Application Granularity using Programmable Data Planes
Key: HGK+21
Author: Rhaban Hark, Mohamed Ghanmi, Ralf Kundel, Patrick Lieser, Ralf Steinmetz
Date: July 2021
Kind: In proceedings
Publisher: IEEE
Book title: Proceedings of 2021 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN)
Abstract: 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.
View Full paper (PDF) | Download Full paper (PDF)

The documents distributed by this server have been provided by the contributing authors as a means to ensure timely dissemination of scholarly and technical work on a non-commercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, not withstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.