Enhancing the Performance of Flow Classification in SDN-Based Intelligent Vehicular Networks
Mahdi Abbasi, Hajar Rezaei, Varun G. Menon, Lianyong Qi, Mohammad R. Khosravi
Abstract
Intelligent vehicular networks converged with software-defined networking provides several flow-based surveillance services to mobile applications on vehicular nodes. But, as the scale of such networks grows exponentially, a substantial delay in processing tremendous flows emerges. The delay can be reduced by accelerating the packet classification methods, which are nowadays exploited in software-defined vehicular networks. Fast packet classification lets firewalls to inspect each incoming packet at wire speed. One of the well-known packet classification methods is the KD-tree algorithm. This paper presents an enhanced version of this algorithm that uses the geometric space to display different fields and increases search speed by recursive decomposition of the search space. Also, the enhanced KD-tree is integrated with a leaf-pushing technique, which enhances the performance of KD-tree search during classification. The proposed algorithm is implemented using a bloom filter data structure and a hash table. Experimental results show that the proposed leaf-pushed KD-tree algorithm improves packet classification speed up to 24 times in comparison with the conventional KD-tree. Moreover, the proposed algorithm can significantly reduce the classification time in comparison with state-of-the-art tree-based algorithms.