Boundary Cutting for Packet Classification

Lim, Hyesook; Lee, Nara; Jin, Geumdan; Lee, Jung Won; Choi, Young-Ju; Yim, Changhoon

doi:10.1109/tnet.2013.2254124

Cited by 34 publications

(16 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The representative algorithms are Hierarchical Intelligent Cuttings[15], HyperCuts[16], Recursive Flow Classification[17], GroupCuts[18], unsupervised co-clustering algorithm[19] and so on.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical trie packet classification algorithm based on expectation-maximization clustering

Zhao

2017

PLoS ONE

View full text Add to dashboard Cite

With the development of computer network bandwidth, packet classification algorithms which are able to deal with large-scale rule sets are in urgent need. Among the existing algorithms, researches on packet classification algorithms based on hierarchical trie have become an important packet classification research branch because of their widely practical use. Although hierarchical trie is beneficial to save large storage space, it has several shortcomings such as the existence of backtracking and empty nodes. This paper proposes a new packet classification algorithm, Hierarchical Trie Algorithm Based on Expectation-Maximization Clustering (HTEMC). Firstly, this paper uses the formalization method to deal with the packet classification problem by means of mapping the rules and data packets into a two-dimensional space. Secondly, this paper uses expectation-maximization algorithm to cluster the rules based on their aggregate characteristics, and thereby diversified clusters are formed. Thirdly, this paper proposes a hierarchical trie based on the results of expectation-maximization clustering. Finally, this paper respectively conducts simulation experiments and real-environment experiments to compare the performances of our algorithm with other typical algorithms, and analyzes the results of the experiments. The hierarchical trie structure in our algorithm not only adopts trie path compression to eliminate backtracking, but also solves the problem of low efficiency of trie updates, which greatly improves the performance of the algorithm.

show abstract

Section: Related Workmentioning

confidence: 99%

Hierarchical trie packet classification algorithm based on expectation-maximization clustering

Zhao

2017

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…In the construction of a decision tree of the HiCuts algorithm, an extensive number of cuts expend more stockpiling, and a little number of cuts causes slower search performance [18]. There are no.…”

Section: Hicutsmentioning

confidence: 99%

“…It was introduced in 2003.In this algorithm multiple fields are considered at a time for cutting. Hence which results in fast searching time and decision trees are shorter in depth [18].…”

Section: Hypercutsmentioning

confidence: 99%

See 1 more Smart Citation

A Survey Paper of Novel Approach towards Multidimensional Packet Classification

2016

IJSR

View full text Add to dashboard Cite

“…A rule database is stored in an external memory because of its size as the number of rules increases. Since an access to an external memory takes 10-20 times longer than that to an on-chip memory [2], the packet classification performance directly relates to the number of external memory accesses.…”

Section: Introductionmentioning

confidence: 99%

Packet classification using a bloom filter in a leaf-pushing area-based quad-trie

Lim¹,

Byun²

2015

2015 ACM/IEEE Symposium on Architectures for Networking and Communications Systems (ANCS)

Self Cite

View full text Add to dashboard Cite

Packet classification is one of the most essential functions that Internet routers should perform at wire-speed for every incoming packet. An area-based quad-trie (AQT) for packet classification has an issue in search performance since many rule nodes can be encountered in a search procedure. A leaf-pushing AQT improves the search performance of the AQT by making a single rule node exist in each search path. This paper proposes a new algorithm to improve the search performance of the leaf-pushing AQT further. The proposed algorithm builds a leaf-pushing AQT using a Bloom filter and a hash table stored in on-chip memories. The level of a rule node and a pointer to a rule database are identified by sequentially querying the Bloom filter and by accessing the hash table, respectively.

show abstract

Boundary Cutting for Packet Classification

Cited by 34 publications

References 36 publications

Hierarchical trie packet classification algorithm based on expectation-maximization clustering

Hierarchical trie packet classification algorithm based on expectation-maximization clustering

A Survey Paper of Novel Approach towards Multidimensional Packet Classification

Packet classification using a bloom filter in a leaf-pushing area-based quad-trie

Contact Info

Product

Resources

About