CMT: An Efficient Algorithm for Scalable Packet Classification

Abstract

Packet classification plays an essential role in diverse network functions such as quality of service, firewall filtering and load balancer. However, implementing an efficient packet classifier is a challenging problem. The problem even gets worse in the era of software-defined network, in which frequent rule updates are performed, and complex flow tables are used. This paper proposes CMT, a new software algorithm named by its novel data structure—common mask tree—to implement an efficient multi-field packet classifier. The core idea of CMT is to combine the strengths of both decision-tree and tuple-space schemes by employing tree-like structures and hash tables simultaneously. The objective of CMT is to achieve both high classification performance and fast rule updates. In the evaluation section, CMT is compared with decision-tree and tuple-space schemes. Compared to the state-of-the-art decision-tree methods, CMT performs rule updates at two orders of magnitude faster. CMT has a stable performance on different rulesets and achieves a 40% improvement in memory access compared to the state-of-the-art tuple-space method.

SplitTrie: A Fast Update Packet Classification Algorithm with Trie Splitting

“…Many OpenFlow implementations use linear table and some simple algorithms to implement ternary matching and LPM, such as Tuple Space Search (TSS) [6]. The lookup performances of the algorithms are poor, so large quantities of algorithms have been proposed to solve packet classification problems in recent decades [7][8][9][10][11][12][13][14][15][16][17][18]. However, the match rules will be more flexible in some SDN implementations.…”

“…Dividing by match value, such as ruleset splitting and search space cutting in treebased algorithms [7][8][9]11,13,18]; • Dividing by mask value, such as TSS, TupleMerge [14], CutTSS [17], and CNP3 [16]; • Dividing by other parameters, such as dimensions [10].…”

Robust Packet Classification with Field Missing

“…Much subsequent models are proposed based on these algorithms. Also, some people integrate the algorithms of decision tree and tuple space, trying to combine their advantages as in [4] and [5]. Vitalii Demianiuk et al realize that the packet classification problem is becoming more difficult nowadays [6].…”