Paper Info
Title
Wildcard Fields-Based Partitioning for Fast and Scalable Packet Classification in Vehicle-to-Everything.
Abstract
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).
Year
DOI
Venue
2019
10.3390/s19112563
SENSORS
Keywords
Field
DocType
network security,packet classification,high-speed communication,high scalability
Decision tree,Wildcard,Telecommunications network,Network security,Networking hardware,Electronic engineering,Packet processing,Engineering,Packet classification,Distributed computing,Scalability
Journal
Volume
Issue
ISSN
19
11.0
1424-8220
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Jaehyung Wee100.34
Jin-Ghoo Choi226129.37
Wooguil Pak3379.83