perfSONAR: Enhancing Data Collection through Adaptive Sampling

perfSONAR IS a tool used to monitor and troubleshoot problems in high-speed networks such as Science Demilitarized Zones (DMZs). It is essential to validate that data transfers are performing as expected. However, perfSONAR suffers from the trade-off between the measurement accuracy and the overhead induced by its active testsThis paper presents a scheme that offloads the traffic monitoring to a programmable data plane (PDP) switch. The scheme integrates a PDP switch with perfSONAR, where the switch continuously collects network measurements (e.g., latency, throughput, packet loss rate) and periodically reports the measurements to the perfSONAR archiver. This integration significantly enhances the granularity, visibility, and troubleshooting capabilities of perfSONAR. Additionally, the scheme automates the reporting period according to the variability of the monitored measurements, which eliminates the need of human intervention observed in today’s networks. In contrast to traditional schemes that report all measurements, the proposed approach uses the Linear Prediction (LP) method to only report the samples that reveal a variation on the measurements. Experimental results show that the system reduces the number of reports by five times under stable network conditions and sustains a relative mean error (RME) below 0.06.