SHIP: A Scalable High-Performance IPv6 Lookup Algorithm That Exploits Prefix Characteristics
Why this work is in the frame
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Bibliographic record
Abstract
Due to the emergence of new network applications, current IP lookup engines must support high bandwidth, low lookup latency, and the ongoing growth of IPv6 networks. However, the existing solutions are not designed to address jointly these three requirements. This paper introduces SHIP, an IPv6 lookup algorithm that exploits prefix characteristics to build a data structure designed to meet future application requirements. Based on the prefix length distribution and prefix density, prefixes are first clustered into groups sharing similar characteristics and then encoded in hybrid trie-trees. The resulting memory-efficient and scalable data structure can be stored in low-latency memories and allows the traversal process to be parallelized and pipelined in order to support high packet bandwidth in hardware. In addition, SHIP supports incremental updates. Evaluated on real and synthetic IPv6 prefix tables, SHIP has a logarithmic scaling factor in terms of the number of memory accesses and a linear memory consumption scaling. Compared with other well-known approaches, SHIP reduces the required amount of memory per prefix by 87%. When implemented on a state-of-the-art field-programmable gate array (FPGA), the proposed architecture can support processing 588 million packets per second.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it