TCAM-Based Multi-Match Packet Classification Using Multidimensional Rule Layering

Chang, Dao-Yuan; Wang, Pi-Chung

doi:10.1109/tnet.2015.2411274

Cited by 15 publications

(7 citation statements)

References 46 publications

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“…Our scheme stores the most representative field of a ruleset in TCAM for indexing the complete rules stored in SRAM. It is based on our previous work in [18] involving multimatch packet classification, a technique for yielding all the matching entries in TCAM for any search key, as not all rules can be distinguished using the same field. Although our scheme may require additional TCAM accesses as a tradeoff, it consumes much less storage than the basic implementation of TCAM-based packet classification.…”

Section: Introductionmentioning

confidence: 99%

Packet Classification Using TCAM of Narrow Entries

Lin,

Pan,

Wang

2023

Technologies

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Packet classification based on rules of packet header fields is the key technology for enabling software-defined networking (SDN). Ternary content addressable memory (TCAM) is a widely used hardware for packet classification; however, commercially available TCAM chips have only limited storage. As the number of supported header fields in SDN increases, the number of supported rules in a TCAM chip is reduced. In this work, we present a novel scheme to enable packet classification using TCAM with entries that are narrower than rules by storing the most representative field of a ruleset in TCAM. Due to the fact that not all rules can be distinguished using one field, our scheme employs a TCAM-based multimatch packet classification technique to ensure correctness. We further develop approaches to reduce redundant TCAM accesses for multimatch packet classification. Although our scheme requires additional TCAM accesses, it supports packet classification upon long rules with narrow TCAM entries, and drastically reduces the required TCAM storage. Our experimental results show that our scheme requires a moderate number of additional TCAM accesses and consumes much less storage compared to the basic TCAM-based packet classification. Thus, it can provide the required scalability for long rules required by potential applications of SDN.

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Section: Introductionmentioning

confidence: 99%

Packet Classification Using TCAM of Narrow Entries

Lin,

Pan,

Wang

2023

Technologies

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“…Some architectural solutions for packet classification suggest the use of Ternary Content Addressable Memory (TCAM) devices [10], [12]- [14] in order to meet performance constraints imposed by high-speed links [2]. However, TCAMs suffer from high cost, high power consumption, storage inefficiency, and limited scalability to long input keys [2].…”

Section: Introductionmentioning

confidence: 99%

Packet Classification Using GPU and One-Level Entropy-Based Hashing

et al. 2020

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The demand for on-line analyzing of internet traffic for both security and QoS consideration directly increases as a function of using diverse applications and as malicious attacks increase. This paper presents a new fast parallel packet classification algorithm based on entropy hashing. The algorithm uses a one-level hashing data structure and enables partitioning a very large rules-set into smaller uniformly distributed sub-rules look-up tables based on maximum entropy and Most Significant Bit (MSB) pattern hash keys. This minimizes the classifier searches only to the relevant appropriate look-up table using the same hash key, and therefore significantly shortens the classification process. A further speed-up factor is achieved by parallelizing the classification algorithm using Nvidia Graphics Processing Unit (GPU). The proposed algorithm is applied to both ACL and FW applications using common synthetic rules-sets of size up to 500k rules. The simulation results show that the proposed algorithm outperforms existing classifiers in terms of both speed up and memory utilization. The required memory size is significantly reduced, and a classification speed-up factor of up to 200 is demonstrated compared to a similar serial approach.

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“…TCAM is similar to BCAM except that it has two SRAM cells which makes it faster than the conventional CAM. It is a widely used memory cells employed in a variety of electronic systems [12] due to their fast searching speed.…”

Section: Introductionmentioning

confidence: 99%

Novel TCAM-based PUF with improved reliability for hardware-entangled security

Tumuganti

Kong

Choi

2017

IEICE Electron. Express

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Physical unclonable functions (PUFs) are the circuits that extract a number of unique chip signatures determined by random physical variations from fabrication. Since they are able to securely store and generate secret keys, PUFs allow to bootstrap the hardware-based implementation of an information security. This manuscript proposes a novel ternary content addressable memory based PUF (TCAM-PUF) for secure embedded systems. The PUF responses are simulated under various conditions, which are start-up values of TCAM cells. The results show that the proposed TCAM-PUF provides higher reliability and uniqueness than SRAM-PUF, which is one of the widely-used memory cell-based weak PUFs. According to our simulation, our TCAM-PUF provides an inter-chip and intra-chip hamming distance of 49.5% and 3.5% (on average), respectively and uniformity of 49.8% (on average).

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TCAM-Based Multi-Match Packet Classification Using Multidimensional Rule Layering

Cited by 15 publications

References 46 publications

Packet Classification Using TCAM of Narrow Entries

Packet Classification Using TCAM of Narrow Entries

Packet Classification Using GPU and One-Level Entropy-Based Hashing

Novel TCAM-based PUF with improved reliability for hardware-entangled security

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