Packet-level adaptive sampling on multi-fluctuation scale traffic

In high-speed Internet links, an effective and efficient packet sampling technology in traffic measurement becomes more and more indispensable for its benefits of reducing data quantity and saving various resources. We address the issue of how to keep or improve sampling accuracy during burst or fluctuation periods. In the paper, an adaptive packet-level sampling method on different traffic fluctuation and burst scales is proposed. The method can dynamically adjust each packet sampling probability dependent on the magnitude of traffic fluctuation. The adjustment granularity is fine because it reaches a single packet. By contrast to the static random sampling method in experiments using real traffic traces, we show that the proposed adaptive sampling has a great capability to trace traffic fluctuations and improve sampling accuracy through capturing more fluctuation or burst details.