PCIU: An efficient packet classification algorithm with an incremental update capability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Packet classification plays a crucial role for a number of network services such as policy based routing, firewalls and traffic billing to name a few. However, classification can be a bottleneck in the above mentioned applications if not implemented properly and efficiently. In this work we propose PCIU, a novel algorithm, which improves upon previous published algorithms. PCIU provides lower pre-processing time, lower memory consumption, ease of incremental rule update, and reasonable classification time compared to published work. The maximum memory to accommodate 10,000 rules in the worst case is less than 2.5 MB. The proposed algorithm was evaluated and compared to several techniques such as RFC and HiCut using several benchmarks. Results obtained indicate that PCIU outperforms these algorithms in terms of speed, memory usage, incremental update capability and pre-processing time.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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