Implementation method for multi-field rule matching

Abstract

The invention discloses an implementation method for multi-field rule matching, and relates to the technical field of high-speed stream classification. The implementation method comprises the steps that firstly, a rule list is established for each tuple according to quintuple definition of IP message data streams, then a Hash rapid stream list is established, and all information matched with rules is added in the rule lists; secondly, IP message data stream matching is conducted, corresponding actions are executed if matched data streams are found, otherwise the rule lists are searched, and mask matching inquiries are conducted. According to the implementation method, the mode that a prefix extension Trie tree high in inquiring speed is improved, part of memory space is sacrificed, and data compression and the Hash list are combined is adopted, and thus under the premise that the inquiring speed is ensured, memory explosion caused by a large-capacity rule base can be avoided.

Description

technical field [0001] The invention relates to high-speed flow classification technology, multi-domain mask matching technology and multi-core processor technology, in particular to an implementation method supporting multi-domain flow rule matching. Background technique [0002] With the increase of network bandwidth and more diversified network service classification, flow-based data packet matching has a wider range of uses. Many key network technologies, such as virtual private network (VPN), firewall based on security access list control, QoS, network intrusion detection and monitoring, network address translation, congestion control, resource reservation, load balancing, collection of statistical data, etc. are all based on packet classification and matching. The 10 Gigabit network has become the core network. In order to improve the network performance in an all-round way, the requirements for the packet classification and matching algorithm as the basic algorithm of...

AI Technical Summary

Problems solved by technology

The various algorithms studied at this stage have obvious advantages and disadvantages. Once the algorithm is extended to a large-scale or even super-large-scale rule base, and multi-domain arbitrary mask matching, many classification algorithms are complicated due to complex methods, many rule restrictions, and complicated calculations. Therefore, the space complexity or time complexity is extremely high, and memory explosion may even occur. Therefore, it is necessary to have a low time complexity and control the storage occupation of the algorithm to avoid conflicts between the two aspects. A reasonable algorithm is adopted, combined with Advantages of hardware devices, so as to find an optimal combination of time complexity and space complexity

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