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).
Highlights
Internet traffic is increasing exponentially every year with the advent of new services such as Internet-of-Things (IoT) and Vehicle-to-Everything (V2X)
We define the coefficient as as possible since the computational burden can degrade the partitioning performance. This algorithm can be applied for most decision tree-based packet classification algorithms and can greatly improve their scalability without sacrificing classification performance
We explain two famous packet classification algorithms based on the decision tree, i.e., HyperCuts [12] and EffiCuts [16]
Summary
Internet traffic is increasing exponentially every year with the advent of new services such as Internet-of-Things (IoT) and Vehicle-to-Everything (V2X). We propose a new partitioning algorithm for decision tree-based classification algorithm to minimize rule duplication significantly. To this end, we define a partitioning preference coefficient for each partitioning field. We define the coefficient as as possible since the computational burden can degrade the partitioning performance This algorithm can be applied for most decision tree-based packet classification algorithms and can greatly improve their scalability without sacrificing classification performance. We propose a new partitioning number per field decision algorithm that chooses two partitioning fields through the partitioning field selection algorithm on each node, and finds the number of partitions based on the selected fields to minimize rule duplication.
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