Traffic Classification via Effective Bit Hashing
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Solution Overview
Problem
Current traffic classification methods, particularly those using hash algorithms, face inefficiencies in rule insertion and search due to high resource consumption and low insertion and search efficiency, especially when dealing with rules containing unconcerned bits that require extensive rule extension and storage.
Innovation Solution
The method determines effective bits within rule sets based on distribution characteristics, using these bits to create hash key values for storing rules in multiple storage units, allowing for parallel search and reducing the need for extensive rule extension, thereby enhancing search performance and reducing resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If hash algorithm is used for traffic classification, then search performance is improved, but resource consumption increases and insertion efficiency decreases
Solution Approach 1:
The patent segments the rule set into multiple subsets based on distribution characteristics of concerned bits. Each subset is stored in a separate storage unit with its own hash table. This segmentation reduces the number of rules each hash table must handle, thereby reducing resource consumption while maintaining high search performance through parallel processing of multiple smaller hash tables.
Solution Approach 2:
The patent introduces a new dimension by creating multiple storage units and hash tables instead of using a single large hash table. By distributing rules across multiple dimensions (storage units), the system achieves better resource utilization and reduces the computational burden on individual hash tables while maintaining overall search efficiency.
2Manufacturing precision
If rule extension is performed to handle unconcerned bits, then rule matching accuracy is improved, but insertion time and storage requirements increase
Solution Approach 1:
The patent extracts only the concerned bits from each rule to create the hash key, leaving unconcerned bits out of the hashing process. This extraction approach maintains rule matching accuracy for relevant fields while avoiding the time-consuming rule extension that would otherwise be required to handle all bits uniformly.
Solution Approach 2:
The patent applies partial action by performing rule extension only when necessary for concerned bits, rather than extending all rules uniformly. By applying the extension operation selectively and partially, the system achieves sufficient matching accuracy without the excessive time cost of comprehensive rule extension.
3Device complexity
If single hash table is used, then device complexity is reduced, but search delay increases due to sequential processing
Solution Approach 1:
The patent segments the single hash table into multiple smaller hash tables distributed across storage units. This segmentation enables parallel search operations across multiple hash tables, significantly reducing search delay. Although device complexity increases slightly due to multiple structures, the performance gain in search speed justifies the added complexity.
Solution Approach 2:
The patent implements periodic action through parallel processing of multiple hash tables. Instead of sequentially processing a single hash table, the system periodically processes multiple hash tables simultaneously, reducing overall search delay through concurrent operations while maintaining manageable device complexity.
Data Source
AI summary
This application provides a traffic classification method and apparatus. The method includes: determining, based on distribution characteristics of concerned bits of a plurality of rules in a first rule set, an effective bit corresponding to the first rule set; determining a hash key value of each rule based on a value of the effective bit of each rule in the first rule set, and storing each rule in the first rule set in at least one of S storage units based on the hash key value, where the first rule set is any one of N rule sets, the N rule sets are stored in the S storage units; and when traffic classification is performed, searching for a corresponding rule in each of the S storage units based on a hash key value of a search key.