부산대학교 도서관

Traffic classification provides substantial benefits for service differentiation, security policy enforcement, and traffic engineering. However, accurately classifying large volumes of network traffic using existing solutions is pretty challenging, as they are typically implemented on commodity servers with slow CPUs for packet processing. To address this, we leverage the opportunity provided by emerging programmable switches and propose HyperClassifier as a solution to achieve accurate, extensible, and scalable traffic classification. HyperClassifier designs an efficient classifying table with an effective flow expiration mechanism that enables lightweight packet inspection on resource-limited switches. We implement an open-source prototype of HyperClassifier on a hardware Tofino switch and conduct extensive evaluations. The results of our evaluation demonstrate that, compared to existing solutions, HyperClassifier can provide orders of magnitude higher classification throughput with comparable classification accuracy.