OpenNetMon: Network monitoring in OpenFlow Software-Defined Networks

TLDR

ISPs typically over‑provision capacity to meet QoS demands, while SDN and OpenFlow enable more efficient network control and flexibility. We present OpenNetMon, an open‑source tool for monitoring per‑flow throughput, delay, and packet loss in OpenFlow networks. OpenNetMon polls edge switches at an adaptive rate that rises with flow‑rate variation and falls when flows stabilize, delivering per‑flow data to verify QoS and supply inputs for TE algorithms. The adaptive polling reduces network and switch CPU overhead while improving measurement accuracy, benefiting both local video‑stream links and aggregated WAN links, and we confirm accurate throughput, delay, and packet‑loss measurements in bursty testbed scenarios.

Abstract

We present OpenNetMon, an approach and open-source software implementation to monitor per-flow metrics, especially throughput, delay and packet loss, in OpenFlow networks. Currently, ISPs over-provision capacity in order to meet QoS demands from customers. Software-Defined Networking and OpenFlow allow for better network control and flexibility in the pursuit of operating networks as efficiently as possible. Where OpenFlow provides interfaces to implement fine-grained Traffic Engineering (TE), OpenNetMon provides the monitoring necessary to determine whether end-to-end QoS parameters are actually met and delivers the input for TE approaches to compute appropriate paths. OpenNetMon polls edge switches, i.e. switches with flow end-points attached, at an adaptive rate that increases when flow rates differ between samples and decreases when flows stabilize to minimize the number of queries. The adaptive rate reduces network and switch CPU overhead while optimizing measurement accuracy. We show that not only local links serving variable bit-rate video streams, but also aggregated WAN links benefit from an adaptive polling rate to obtain accurate measurements. Furthermore, we verify throughput, delay and packet loss measurements for bursty scenarios in our experiment testbed.