Bibliographic record
Abstract
Flow control is critical to the efficient operation of Internet service providers network equipment. In particular, the ability to effectively shape traffic can reduce cost and improve overall customer satisfaction. While such traffic shaping is typically performed by an inline traffic shaper, there are a number of practical cases in which such an inline approach is not feasible. In particular, an inline traffic shaper may reduce reliability or simply be against ISP policy. In these cases third-party flow control is required. Third-party flow control allows the shaper to see all traffic and to inject new traffic into the network. However, it does not allow the shaper to remove or modify existing network data. Within these limitations we study two techniques for flow control, triple-ACK duplication and zero-window-size acknowledgement. We provide analytical justification for why these techniques are promising. In addition, we demonstrate, via simulation, that the zero-window-size technique can reduce bandwidth consumption by 40%, while the triple-ACK duplication can reduce it by up to 85%. These techniques thus offer the possibility for significant flow-control capabilities by a third-party traffic shaper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".