Collecting per-flow aggregates in high-speed links is challenging and usually requires traffic sampling to hand le peak rates and extreme traffic mixes. Static selection of sam pling rates is problematic, since worst-case resource usage is or ders of magnitude higher than the average. To address this issue, adaptive schemes have been proposed in the last few years tha t periodically adjust packet sampling rates to network condi tions. However, such proposals rely on complex algorithms and data structures of costly maintenance. As a consequence, adapti ve sampling is still not widely implemented in routers. We present a novel flow sampling based measurement scheme called Cuckoo Sampling that efficiently collects per-flow ag gre- gates, while smoothly discarding information as it exceeds the available memory. After a measurement epoch, it provides a random sample of the input flows, at a close-to-maximum rate as allowed by the available memory budget. Our proposal relies on a very simple data structure, require s few per-packet operations, has a CPU cost that is independen t of the memory budget and traffic profile, and is suitable for hardware implementation. We back the theoretical analysis of the algorithm with experiments with both synthetic and real net work traffic, and show that our algorithm requires significantly l ess resources than existing adaptive sampling schemes

Peer Reviewed