Multi-match Classification Research Articles

Multi-match Packet Classification Scheme Combining TCAM with an Algorithmic Approach

Packet classification is one of the essential functionalities of Internet routers in providing quality of service. Since the arrival rate of input packets can be tens-of-millions per second, wirespeed packet classification has become one of the most challenging tasks. While traditional packet classification only reports a single matching result, new network applications require multiple matching results. Ternary content-addressable memory (TCAM) has been adopted to solve the multi-match classification problem due to its ability to perform fast parallel matching. However, TCAM has a fundamental issue: high power dissipation. Since TCAM is designed for a single match, the applicability of TCAM to multi-match classification is limited. In this paper, we propose a cost- and energy-efficient multi-match classification architecture that combines TCAM with a tuple space search algorithm. The proposed solution uses two small TCAM modules and requires a single-cycle TCAM lookup, two SRAM accesses, and several Bloom filter query cycles for multimatch classifications.

Efficient Multimatch Packet Classification for Network Security Applications

New network applications like intrusion detection systems and packet-level accounting require multimatch packet classification, where all matching filters need to be reported. Ternary content addressable memories (TCAMs) have been adopted to solve the multimatch classification problem due to their ability to perform fast parallel matching. However, TCAMs are expensive and consume large amounts of power. None of the previously published multimatch classification schemes are both memory and power efficient. In this paper, we develop a novel scheme that meets both requirements by using a new set splitting algorithm (SSA). The main idea behind SSA is that it splits filters into multiple groups and performs separate TCAM lookups into these groups. It guarantees the removal of at least 1/2 the intersections when a filter set is split into two sets, thus resulting in low TCAM memory usage. SSA also accesses filters in the TCAM only once per packet, leading to low-power consumption. We compare SSA with two best known schemes: multimatch using discriminators (MUD) (Lakshminarayanan and Rangarajan, 2005) and geometric intersection-based solutions (Yu and Katz, 2004). Simulation results based on the SNORT filter sets show that SSA uses approximately the same amount of TCAM memory as MUD, but yields a 75%-95% reduction in power consumption. Compared with geometric intersection-based solutions, SSA uses 90% less TCAM memory and power at the cost of one additional TCAM lookup per packet. We also show that SSA can be combined with SRAM/TCAM hybrid approaches to further reduce energy consumption

Efficient Multimatch Packet Classification and Lookup with TCAM

Today's packet classification systems are designed to provide the highest-priority matching result, such as the longest prefix match, even if a packet matches multiple classification rules. However, new network applications demanding multimatch classification - that is, requiring all matching results instead of only the highest-priority match - are emerging. Ternary content-addressable memory is becoming a common extension to network processors, and its capability and speed make it attractive for high-speed networks. The proposed TCAM-based scheme produces multimatch classification results with about 10 times fewer memory lookups than a pure software approach. In addition, their scheme for removing negation in rule sets saves up to 95 percent of the TCAM space used by a straightforward implementation.