Multi-Stride Compressed Trie Lookup Using Hashed Keys
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Solution Overview
Problem
Existing lookup table structures for large networks, such as those using IPv4 and IPv6, face inefficiencies in storage and search time due to the need for multiple levels of binary search in Trie structures, and compression methods do not adequately reduce storage requirements in sparse tables.
Innovation Solution
The use of a hashed key to reduce the number of stride bits needed for lookup, combined with compression functions to optimize storage by representing long input keys in a multi-stride structure, allowing for faster convergence to lookup results with reduced memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a traditional Trie structure with multiple levels of binary search is used, then storage space efficiency is improved, but search time increases
Solution Approach 1:
The patent divides the lookup key into multiple strides (segments) and processes them in parallel through a multi-stride compressed trie structure. Each stride processes a portion of the key simultaneously, reducing the sequential search steps while maintaining storage efficiency through compression techniques.
Solution Approach 2:
The patent transitions from a single-dimensional binary search approach to a multi-dimensional structure where multiple strides operate in parallel. This dimensional expansion allows simultaneous processing of different key portions, improving search speed without sacrificing storage efficiency.
2Quantity of substance
If compression functions are applied to reduce storage requirements in sparse tables, then storage efficiency is improved, but lookup complexity increases
Solution Approach 1:
The patent pre-computes and stores compression function results in the multi-stride compressed trie structure during table construction. This preliminary action eliminates the need for complex runtime compression operations, reducing lookup complexity while maintaining storage efficiency.
Solution Approach 2:
The multi-stride compressed trie structure inherently incorporates compression logic in its construction, making the structure self-sufficient. The compression and lookup operations are integrated into the structure itself, eliminating the need for separate complex compression routines during lookup.
3Speed
If the number of stride bits is reduced using hashed keys, then lookup speed is improved, but collision handling complexity increases
Solution Approach 1:
The patent merges the hash function output with the multi-stride trie structure, combining collision resolution directly into the trie nodes. This integration allows rapid lookup through the hashed key while handling collisions through the existing trie structure, reducing overall complexity.
Solution Approach 2:
The multi-stride compressed trie structure acts as an intermediary between the hashed key and the final lookup result. It resolves hash collisions by distributing them across multiple strides, simplifying collision handling while maintaining fast lookup performance.
Data Source
AI summary
An input lookup key is hashed and the hashed key divided into stride bits into a multi-level Trie structure. A compression function logically combines the stride bits to generate the compressed index bits into the stride tables. The bucket in the last stride table found by the hashed key may have several keys that collide at the same hash value. Discriminant bits are read from the key and select a stored key in the bucket table for verification of its result. Since the hashed key is a compression of the longer input key, more information is contained per bit of the hashed key than in the long key. The multi-stride lookup is performed first on the hashed key, allowing a faster convergence to the lookup result. The first stride can index a single hash table, with the remaining hash bits and discriminant bits used to select from among colliding keys.