Towards Optimized Packet Classification Algorithms for Multi-Core Network Processors

2007 ◽  
Author(s):  
Yaxuan Qi ◽  
Bo Xu ◽  
Fei He ◽  
Xin Zhou ◽  
Jianming Yu ◽  
...  
Keyword(s):  
Packet Classification ◽  
Classification Algorithms ◽  
Core Network ◽  
Network Processors

Extensions to decision-tree based packet classification algorithms to address new classification paradigms

2017 ◽  
Vol 122 ◽  
pp. 83-95 ◽  
Author(s):  
Thibaut Stimpfling ◽  
Normand Bélanger ◽  
Omar Cherkaoui ◽  
André Béliveau ◽  
Ludovic Béliveau ◽  
...  
Keyword(s):  
Decision Tree ◽  
Packet Classification ◽  
Classification Algorithms ◽  
New Classification

scholarly journals A Fast, Smart Packet Classification Algorithm Based on Decomposition

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Chuanhong Li ◽  
Xuewen Zeng ◽  
Lei Song ◽  
Yan Jiang
Keyword(s):  
Vital Role ◽  
Packet Classification ◽  
Classification Algorithm ◽  
Set Partitioning ◽  
Experimental Results ◽  
Classification Algorithms ◽  
Memory Consumption ◽  
Memory Overhead ◽  
Rule Sets ◽  
Rule Set

Packet classification algorithms have been the focus of research for the last few years, due to the vital role they play in various services based on packet forwarding. However, as the number of rules in the rule set increases, not only the preprocessing time but also the memory consumption is increasing greatly. In this paper, we first model and analyze the above issue in depth. Then, a fast, smart packet classification algorithm based on decomposition is proposed. By boundary-based rule traversal and smart rule set partitioning, both the preprocessing time and memory consumption are reduced dramatically. Experimental results show that the preprocessing time of our method achieves 8.8-time improvement at maximum compared with the PCIU and achieves about 31.5-time improvement on average compared with CutSplit for large rule sets. Meanwhile, the memory overhead is reduced by 40% at maximum and 27.5% on average compared with the PCIU.

scholarly journals Wildcard Fields-Based Partitioning for Fast and Scalable Packet Classification in Vehicle-to-Everything

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2563 ◽  
Author(s):  
Jaehyung Wee ◽  
Jin-Ghoo Choi ◽  
Wooguil Pak
Keyword(s):  
Decision Tree ◽  
Communication Networks ◽  
High Speed ◽  
Classification Performance ◽  
Packet Classification ◽  
Classification Algorithms ◽  
Packet Processing ◽  
Security Issues ◽  
Security Of Network ◽  
High Level

Vehicle-to-Everything (V2X) requires high-speed communication and high-level security. However, as the number of connected devices increases exponentially, communication networks are suffering from huge traffic and various security issues. It is well known that performance and security of network equipment significantly depends on the packet classification algorithm because it is one of the most fundamental packet processing functions. Thus, the algorithm should run fast even with the huge set of packet processing rules. Unfortunately, previous packet classification algorithms have focused on the processing speed only, failing to be scalable with the rule-set size. In this paper, we propose a new packet classification approach balancing classification speed and scalability. It can be applied to most decision tree-based packet classification algorithms such as HyperCuts and EffiCuts. It determines partitioning fields considering the rule duplication explicitly, which makes the algorithm memory-effective. In addition, the proposed approach reduces the decision tree size substantially with the minimal sacrifice of classification performance. As a result, we can attain high-speed packet classification and scalability simultaneously, which is very essential for latest services such as V2X and Internet-of-Things (IoT).

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