Deterministic Approach for Range-enhanced Reconfigurable Packet Classification Engine

Abstract

Reconfigurable hardware is a promising technology for implementing firewalls, routing mechanisms, and new protocols for evolving high-performance network systems. This work presents a novel deterministic approach for a Range-enhanced Reconfigurable Packet Classification Engine based on the number of rules on FPGAs. The proposed framework uses a RAM-established Ternary Match to represent the prefix and the range prefix and efficient rule-reordering for priority selection to get both best-match and multi-match in the same architecture. The recommended framework exhibits 3.2 Mbits of LUT-RAM-based ternary content addressable memory (TCAM) to hold a maximum of 31.3 K of 104- bit rules with 520 MPPS . LUT-RAM, along with BRAM, shows 4 Mbits of TCAM space to implement 38.5 K of 104- bit rules to sustain a throughput of 400 MPPS on Virtex-7 FPGA. The complete architecture offers scalability, better resource utilization (minimum of 50% ), representation of inverse prefix with single entry, range expansion with a single rule, getting best- and multi-match, and determination of the required number of FPGA resources for a particular dataset.