RFC: Range feature code for TCAM-based packet classification

Sun, Peng; Lan, Julong; Wang, Peng; Ma, Teng

doi:10.1016/j.comnet.2017.02.016

Cited by 6 publications

(13 citation statements)

References 16 publications

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“…For example, the tuple (1, 3) must have a marker that produces R5 rule in the tuple (2,3). Also, the tuple (1, 3) must have a marker of R9 rule related to the tuple (2,4). Figure 3 shows the Kd-tree of the rules in Table 1 In the search for the best matching rule from the previous example, traversal cannot find any matches in the tuple (3,2) and continues on the left.…”

Section: Use Of Markers In the Kd-tree Algorithmmentioning

confidence: 99%

“…When matched against (1, 3), the search is successful due to the presence of the R9 marker. Finally, the search algorithm continues to traverse the right-hand sub-tree and reports R9 rule in the tuple (2,4) as the best matching rule.…”

Section: Use Of Markers In the Kd-tree Algorithmmentioning

confidence: 99%

“…In this example, traversal starts from the root towards the right-hand child of (4, 4). Then the tuple (5,4) and, finally, the tuple (5, 5) are traversed. Finally, as the traversal path reaches a leaf node, the rules of that node are also examined and R6 rule is reported as the result.…”

Section: Pruned Kd-tree Algorithmmentioning

confidence: 99%

“…Since a packet may be matched by several rules, the action determined by the rule with the highest priority will be performed. For example, one priority can be the length of the prefix of the source or destination IP addresses or the order of the appearance of a rule in a rule set [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“…Implementation and evaluation of the proposed method are explained in Sect. 4. The final section of the paper is dedicated to concluding and presenting solutions for further development of research in this field.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Pruned Kd-tree: a memory-efficient algorithm for multi-field packet classification

Rafiee

Abbasi

2019

SN Appl. Sci.

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Packet classification is a basic process in most network-based packet processing systems. The key operation in this process is to match the packet header against the rules defined in a rule-set and, finally, to find the best matching rule. One of the well-known algorithms for packet classification is the Kd-tree algorithm. This algorithm produces a binary tree using the tuples created by the length of the prefix of the source and destination IP addresses of the rules. The tree is intended to classify the packets. The efficiency of the mechanism for producing and searching the binary tree in this algorithm is affected by two major disadvantages, namely, redundant nodes and redundant accesses during the search. The former increases memory consumption and the latter slows down packet classification. The proposed idea in this article is to prune redundant nodes by sorting the rules corresponding to each tuple in the tree nodes. The experimental results suggest that the throughput rate of the pruned Kd-tree is 43.64% higher than the Kd-tree. Also, by pruning the structure of the Kd-tree, our proposed method could at best reduce 24% of the memory consumed for the storage of the data structure of the Kd-tree.

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Section: Use Of Markers In the Kd-tree Algorithmmentioning

confidence: 99%

Section: Use Of Markers In the Kd-tree Algorithmmentioning

confidence: 99%

Section: Pruned Kd-tree Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Pruned Kd-tree: a memory-efficient algorithm for multi-field packet classification

Rafiee

Abbasi

2019

SN Appl. Sci.

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A survey of low-latency transmission strategies in software defined networking

Yan

Liu

Shen

et al. 2021

Computer Science Review

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An Adaptive and Lightweight Update Mechanism for SDN

Lan

Guo

et al. 2019

IEEE Access

Self Cite

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To improve the network resources utilization and the quality of service, the provision of an adaptive and customizable network service is deemed a feasible approach. In this paper, based on the quality of service (QoS)-aware traffic classification and real-time network status, an adaptive update mechanism is presented to change the traditional rigid update techniques in software-defined networking. The developed update mechanism aims at abstracting the common update mechanism into update operations and calculates the update sequence on the operation granularity. The mechanism has three work modes, and each mode has a corresponding algorithm. It can adjust the work modes adaptively based on the network condition and the flow QoS requirements to improve the performance. The experimental results demonstrate that the three work modes can achieve optimal performance in ternary content addressable memory (TCAM) overhead reduction, delay, and bandwidth consumption, respectively. For example, when the tri-fusion work mode is leveraged, it provides at least an 85% reduction of the additional TCAM overhead and improves by at least 9%, 65%, and 82% compared to other work modes and the compared algorithms.

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RFC: Range feature code for TCAM-based packet classification

Cited by 6 publications

References 16 publications

Pruned Kd-tree: a memory-efficient algorithm for multi-field packet classification

Pruned Kd-tree: a memory-efficient algorithm for multi-field packet classification

A survey of low-latency transmission strategies in software defined networking

An Adaptive and Lightweight Update Mechanism for SDN

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