UA-Sketch: An Accurate Approach to Detect Heavy Flow based on Uninterrupted Arrival

Jin Ye, Lin Li, Wenlu Zhang, Guihao Chen, Yuanchao Shan, Yijun Li, Weihe Li, Jiawei Huang

Abstract: Heavy flow detection in enormous network traffic is a critical task for network measurement. Due to the limited memory size and high link capacity, accurate detection of heavy flows becomes challenging in large-scale networks. Almost all existing approaches of detecting heavy flows use single-dimension statistics of flow size to make flow-replacement decisions. However, under the mass number of small flows, the heavy flows are prone to be frequently and mistakenly replaced, resulting in unsatisfactory accuracy. To solve this problem, we reveal that the number of uninterrupted arrival packets is a useful metric in identifying flow types. We further propose UA-Sketch that expels small flows and protects heavy ones according to the multiple-dimension statistics including both estimated flow size and number of uninterrupted arrival packets. The test results of trace-driven simulations and OVS experiments show that, even under small memory, UA-Sketch achieves higher accuracy than the existing works, with the F1 Score by up to 2.1 ×.