Heavy-Hi er Detection Entirely in the Data Plane

Identifying the “heavy hitter” ows or ows with large tra c volumes in the data plane is important for several applications e.g., ow-size aware routing, DoS detection, and tra c engineering. However, measurement in the data plane is constrained by the need for linerate processing (at 10-100Gb/s) and limited memory in switching hardware. We propose HashPipe, a heavy hitter detection algorithm using emerging programmable data planes. HashPipe implements a pipeline of hash tables which retain counters for heavy ows while evicting lighter ows over time. We prototype HashPipe in P4 and evaluate it with packet traces from an ISP backbone link and a data center. On the ISP trace (which contains over 400,000 ows), we nd that HashPipe identi es 95% of the 300 heaviest ows with less than 80KB of memory.