Space-Efficient TCAM-Based Classification Using Gray Coding

Ternary content-addressable memories (TCAMs) are increasingly used for high-speed packet classification. TCAMs compare packet headers against all rules in a classification database in parallel and thus provide high throughput unparalleled by software-based solutions. TCAMs are not well-suited, however, for representing rules that contain range fields. Such rules have to be represented by multiple TCAM entries. The resulting range expansion can dramatically reduce TCAM utilization. The majority of real-life database ranges are short. We present a novel algorithm called short range gray encoding (SRGE) for the efficient representation of short range rules. SRGE encodes range borders as binary reflected gray codes and then represents the resulting range by a minimal set of ternary strings. SRGE is database independent and does not use TCAM extra bits. For the small number of ranges whose expansion is not significantly reduced by SRGE, we use dependent encoding that exploits the extra bits available on today´s TCAMs. Our comparative analysis establishes that this hybrid scheme utilizes TCAM more efficiently than previously published solutions. The SRGE algorithm has worst-case expansion ratio of 2W-4, where W is the range-field length . We prove that any TCAM encoding scheme has worst-case expansion ratio W or more.