Definition and requirements of flow classification algorithms
Give a formal definition of flow classification: the classifier has N rules {Rj, 1≤j≤N}, the rule Rj consists of 3 parts: 1) regular expression Rj [i], 1≤i≤k; 2) priority Level pri (Rj); 3) The operation performed acTIon (Rj). For each data packet received, it can be regarded as a point in the k-dimensional space (P1, P2, ..., Pk).
The problem of k-dimensional flow classification is to find the rule that matches the point (P1, P2, ..., Pk) in all rules, and the rule with the highest priority Rm, that is, pri (Rm)> pri (Rj), ∀j ≠m, 1≤m≤N, 1≤j≤N, and ∀ (1≤i≤k), Pi matches Pj [i], then Rm is said to be the best matching rule.
The requirements of the flow classification algorithm can be summarized as the following 5 points:
1) Fast processing speed. At present, the physical link speed has been greatly improved, and higher requirements are placed on the network processor. The complexity of the flow classification algorithm makes it a bottleneck that limits the processing speed of routers and switches.
2) The required storage capacity is low. In the case of low storage capacity requirements, you can use fast but expensive memory technology.
3) The update time is short. Due to the huge amount of data traffic on the network, the flow classification algorithm must update the data structure quickly to adapt to the frequent changes of the classifier.
4) The definition of rules is flexible. A good flow classification algorithm should support various forms of rules, including exact matching, prefix matching, and range matching in various areas of the packet header.
5) The rules are extensible. The classifier must be able to expand the number of domains on which the classification is based, the length of the domain, and the number of rules to meet the needs of future networks, especially the transition from IPv4 to IPv6.
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