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Clustering-based many-field packet classification in Software-Defined Networking
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DOI：10.1016/j.jnca.2019.102428.png)
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Packet classification is one of the main core functions of networking. With the advent of Software-Defined Networking, packet classification has become more challenging by introducing many-field rulesets. In this paper, we propose an algorithm, Clustering-Based Packet Classification (CBPC), which divides ruleset into some clusters using a new hybrid clustering method, based on an innovative bit-level view. Those rules that have more common wildcard and non-wildcard bit positions are put into the same cluster. Each cluster uses common non-wildcard positions of its rules to produce keys for a hash table insertion and query stages. This makes possible to use hash tables without involving difficulties with inserting and querying ternary vectors, because our algorithm converts it to simple binary operations. In fact, we ignore a portion of information of the rules in key production for the hash tables. It overcomes the problem of extending wildcards to all of the possible values. It is true that some information is lost but it is covered by full matching at the hash table entries. We propose two versions for CBPC, online and offline. The online version supports update, which is an important requirement for today's packet classification algorithms. The proposed algorithm is evaluated and compared with some well-known and state-of-the-art algorithms by extensive simulations. The results show that Online-CBPC achieves 197% higher throughput and 64% faster update than Tuple Space Search, OpenVSwitch standard algorithm, while using almost the same amount of memory.
Keyword:
Many-field packet classification
Software-defined networking
OpenFlow
Hierarchical clustering
Machine learning
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