FEACAN: Front-end acceleration for content-aware network processing

Modern networks are increasingly becoming content aware to improve data delivery and security via content-based network processing. Content-aware processing at the front end of distributed network systems, such as application identification for datacenter load-balancers and deep packet inspection for security gateways, is more challenging due to the wire-speed and low-latency requirement. Existing work focuses on algorithm-level solutions while lacking system-level design to meet the critical requirement for front-end content processing. In this paper, we propose a system-level solution named FEACAN for front-end acceleration of content-aware network processing. FEACAN employs a software-hardware co-design supporting both signature matching and regular expression matching for content-aware network processing. A two-dimensional DFA compression algorithm is designed to reduce the memory usage and a hardware lookup engine is proposed for high-performance lookup. Experimental results show that FEACAN achieves better performance than existing work in terms of processing speed, resource utilization, and update time.