TupleTree: A High-Performance Packet Classification Algorithm Supporting Fast Rule-Set Updates

Packet classification plays a crucial role in various network functions such as access control and routing. In recent years, the rapid development of SDN and NFV poses new challenges for packet classification to support fast rule-set updates as introducing strong dynamics for the structure of networ...

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Veröffentlicht in:IEEE/ACM transactions on networking Jg. 31; H. 5; S. 1 - 15
Hauptverfasser: Zhong, Jincheng, Wei, Ziling, Zhao, Shuang, Chen, Shuhui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.10.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Access control
Algorithms
Classification
decision tree
Decision trees
Heuristic algorithms
IEEE transactions
Memory management
Merging
Packet classification
rule-set update
Software
Splitting
tuple space search
Vegetation
ISSN:1063-6692, 1558-2566
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Zusammenfassung:Packet classification plays a crucial role in various network functions such as access control and routing. In recent years, the rapid development of SDN and NFV poses new challenges for packet classification to support fast rule-set updates as introducing strong dynamics for the structure of networks. To this end, this paper proposes a novel scheme, TupleTree, to perform high-speed packet classification while providing fast rule-set update ability. TupleTree is a hybrid scheme combining decision tree and tuple space. In TupleTree, it organizes rules in a decision tree-like structure, but distributes rules in each node into child nodes through hashing rather than cutting or splitting. With the decision tree structure, for each classification, one leaf node containing a few rules can be rapidly indexed. Hence, a high classification performance can be achieved. Meanwhile, with hashing instead of cutting or splitting, it is easy to support fast rule-set updates due to having avoided the rule replication problem. Compared to state-of-the-art schemes that support fast rule-set updates, experimental results show that our proposed scheme achieves a classification performance improvement of 85% to 237% while retaining close update performance for large rule-sets.
Bibliographie:ObjectType-Article-1
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ISSN:1063-6692
1558-2566
DOI:10.1109/TNET.2022.3227206