Abstract:

In the research on packet classification technology,designing an efficient index structure to achieve fast packet matching is the key to effective packet classification.Therefore,in order to improve the memory utilization of packet classification technology based on hash-based methods,a rule storage matching model is proposed to achieve more uniform mapping and reduce hash collisions.This model can support more uniform rule storage mapping within a classification subset.Based on this model,a packet classification algorithm is proposed,which uses the Prefix Length Reduction algorithm to divide the ruleset into multiple subsets,and then the same storage structure is allocated for each subset.This storage structure includes an identification information processing unit,a mapping model unit and a rule query matching unit.In the packet classification stage,the identification information processing unit converts the packet headers into multi-dimensional vectors and then these vectors are input to the mapping model unit.Based on the output of the model,the matching rules can be retrieved from the rule query matching unit.Experimental results show that,compared with the existing algorithms,the proposed algorithm doubles the classification throughput,reduces the storage consumption by about 20% on average and increases the update speed by 3 times on average with the uniformity of the rule mapping also improved.

Key words: packet classification, rule storage matching model, index structure design