OpenMeasure: Adaptive flow measurement & inference with online learning in SDN
Chang Liu, AMehdi Malboubi, Chen‐Nee Chuah · 2016
Accurate and efficient network-wide traffic measurement is crucial for network management. Recently, Software-defined networking (SDN) has opened up new opportunities in network measurement and inference. In this work, we demonstrate an efficient flow measurement and inference framework which performs adaptive measurement with online learning. Using the reprogrammability of SDN, we assist network inference with online learning predictions and dynamically update the measurement rules network-wide to track and measure the most informative flows. To best utilize the available measurement resources, we leverage the SDN controller (with its global view) to optimally place flow monitoring rules across network switches. Using real-world data, we show that our measurement framework achieves high performance in both estimating the traffic matrix and identifying hierarchical heavy hitters.