Packet classification using diagonal-based tuple space search

Lee, Fu-Yuan; Shieh, Shiuh-Pyng

doi:10.1016/j.comnet.2005.06.012

Cited by 4 publications

(1 citation statement)

References 20 publications

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“…For example a filter with 3 fields, the tuple [3, 1, 2] present the tuple specification, the tuple specification can be divided into different tuple groups. So the filters that have the same tuple group have the same tuple specification [2] [10].…”

Section: Tuple-space Search (Tss)mentioning

confidence: 99%

Comparison Study Of Packet Classification Algorithms In Wired Networks

Mahmoud¹,

Ali²

2012

J.Edu.Sci.

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The packet classification technique play an important role for many internet advanced services that require the capability to distinguish traffic in different flows, such as routers and security services like firewalls and packet filters. This paper provide a brief introduction for packet classification process. Beside, the categorization of the algorithms that classify packets to different flow. Also, this paper give a brief description of two homogenous algorithms (algorithms that belongs to same category), these algorithms are Tuple Space Search (TSS) and Recursive Flow Classification (RFC). Furthermore, this paper provides a comparative

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Section: Tuple-space Search (Tss)mentioning

confidence: 99%

Comparison Study Of Packet Classification Algorithms In Wired Networks

Mahmoud¹,

Ali²

2012

J.Edu.Sci.

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CMT: An Efficient Algorithm for Scalable Packet Classification

Chen

Zhong

Huang

et al. 2021

The Computer Journal

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Packet classification plays an essential role in diverse network functions such as quality of service, firewall filtering and load balancer. However, implementing an efficient packet classifier is a challenging problem. The problem even gets worse in the era of software-defined network, in which frequent rule updates are performed, and complex flow tables are used. This paper proposes CMT, a new software algorithm named by its novel data structure—common mask tree—to implement an efficient multi-field packet classifier. The core idea of CMT is to combine the strengths of both decision-tree and tuple-space schemes by employing tree-like structures and hash tables simultaneously. The objective of CMT is to achieve both high classification performance and fast rule updates. In the evaluation section, CMT is compared with decision-tree and tuple-space schemes. Compared to the state-of-the-art decision-tree methods, CMT performs rule updates at two orders of magnitude faster. CMT has a stable performance on different rulesets and achieves a 40% improvement in memory access compared to the state-of-the-art tuple-space method.

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HaRP: Rapid Packet Classification via Hashing Round-Down Prefixes

Pong

Tzeng

2011

IEEE Trans. Parallel Distrib. Syst.

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Packet classification using diagonal-based tuple space search

Cited by 4 publications

References 20 publications

Comparison Study Of Packet Classification Algorithms In Wired Networks

Comparison Study Of Packet Classification Algorithms In Wired Networks

CMT: An Efficient Algorithm for Scalable Packet Classification

HaRP: Rapid Packet Classification via Hashing Round-Down Prefixes

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