Exploiting Non-Uniformities in Redundant Traffic Elimination
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
Protocol-independent redundant traffic elimination (RTE) at the network layer is a method of detecting and removing redundant chunks of data from data packets using caching at both ends of a network link or path. In this paper, we propose a set of techniques to improve the effectiveness of packet-level RTE. In particular, we consider two bypass techniques, with one based on packet size, and the other based on content type. Both bypass techniques are effective in reducing the processing requirements of RTE, with little or no adverse impact on redundancy detection. The bypass techniques apply at the front-end of the RTE pipeline. Within the RTE pipeline, we propose chunk overlap and oversampling as techniques that can improve redundancy detection, while obviating the storage and processing requirements associated with chunk expansion at the network endpoints as suggested by previous research. Finally, we propose savings-based cache management at the backend of the RTE pipeline, as an improvement to the commonly used FIFO-based cache management. We evaluate our techniques on full-payload packet-level traces from a university environment. Our results show that the 11-12% savings achieved with typical RTE can be improved to 16-18% with our techniques.
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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 | 0.000 | 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