Self-Adaptive Sampling for Network Traffic Measurement

Per-flow traffic measurement in the high-speed network plays an important role in many practical applications. Due to the limited on-chip memory and the mismatch between off-chip memory speed and line rate, sampling-based methods select and forward a part of flow traffic to off-chip memory, complementing sketch-based solutions in estimation accuracy and online query support. However, most current work uses the same sampling probability for all flows, overlooking that the sampling rates different flows require to meet the same accuracy constraint are different. It leads to a waste in storage and communication resources. In this paper, we present self-adaptive sampling, a framework to sample each flow with a probability adapted to flow size/spread. Then we propose two algorithms, SAS-LC and SAS-LOG, which are geared towards per-flow spread estimation and per-flow size estimation by using different compression functions. Experimental results based on real Internet traces show that, when compared to NDS in per-flow spread estimation, SAS-LC can save around 10% on-chip space and reduce up to 40% communication cost for large flows. Moreover, SAS-LOG can save 40% on-chip space and reduce up to 96% communication cost for large flows than NDS in per-flow size estimation.