Scalable Multi-Match Packet Classification Using TCAM and SRAM

Abstract

Packet classification is an enabling technology for various network services. Fast single-match packet classification can be achieved by using ternary content addressable memory (TCAM) because of the superior speed performance. TCAM has some drawbacks including incapability to store arbitrary ranges, confined TCAM capacity and limited choices of entry lengths. Moreover, TCAM only reports the first matching entry to impose a limitation on supporting multi-match packet classification, which requires all matching rules. The existing algorithms deal with the issues of TCAM-based multi-match packet classification by burdening TCAM with extra entries and/or accesses. In this work, we offload the overhead of TCAM to static random access memory (SRAM) to achieve efficient multi-match packet classification. Our scheme synthesizes TCAM compatible entries by using binary decision trees and employs SRAM for further comparisons. Each synthesized entry can be stored in one TCAM entry to significantly reduce TCAM consumption and fulfill low power consumption. The experimental results show that our scheme can lower the demand of TCAM to improve both search latency and energy efficiency. The scalability of TCAM-based multi-match packet classification can thus be improved drastically.