On the performance of Redundant Traffic Elimination in WLANs
Bibliographic record
Abstract
Redundant Traffic Elimination (RTE) detects and removes repeated chunks of data across network flows, protocols, and applications, with the purpose of reducing bandwidth usage. In this paper, we explore the effectiveness of RTE in WLAN, compare it to RTE in Ethernet, and investigate specific issues affecting RTE in WLAN. Our results show that applying RTE to WLAN links is promising and can potentially yield high bandwidth savings, although RTE is not as effective in WLAN as in wired networks. However, to exploit the full potential of RTE, it is necessary to deal with specific challenges, such as longer headers, control and management frames, retransmissions, and dropped frames. We find that including parts of MAC headers in RTE can increase overall bandwidth savings by up to 53% in a public WLAN. To handle dropped frames, which can severely compromise the effectiveness of RTE, we make a case for MAC-layer RTE, which detects frame loss at the sender. This preserves 23% more savings than a previous approach. However, frame retransmissions generate additional traffic at MAC layer, which reduces the effectiveness of RTE in general case.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".