High Throughput Sketch Based Online Heavy Hitter Detection on FPGA

In the context of networking, a heavy hitter is an entity in a data stream whose amount of activity (such as bandwidth consumption or number of connections) is higher than a given threshold. Detecting heavy hitters is a critical task for network management and security in the Internet and data centers. Data streams in modern network usually contain millions of entities, such as traffic flows or IP domains. It is challenging to detect heavy hitters at a high throughput while supporting such a large number of entities. I this work, we propose a high throughput online heavy hitter detector based on the Count-min sketch algorithm on FPGA. We propose a high throughput hash computation architecture, optimize the Count-min sketch for hardwarebased heavy hitter detection and use forwarding to deal with data hazards. The post place-and-route results of our architecture on a state-of-the-art FPGA shows high throughput and scalability. Our architecture achieves a throughput of 114 Gbps while supporting a typical 1 M concurrent entities. It sustains 100+ Gbps throughput while supporting various number of concurrent entities, stream sizes and accuracy requirements. Our implementation demonstrates improved performance compared with other sketch acceleration techniques on various platforms using similar sketch configurations.