TCP NewReno: Slow-but-Steady or Impatient?
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
In this paper, we compare the throughputs of two different TCP NewReno variants, namely Slow-but-Steady and Impatient. We develop analytic throughput models of these variants as a function of round-trip time, loss event rate, and the burstiness of packet drops within a loss event. Our models build upon prior work on TCP Reno throughput modeling, but extend this work to provide an analytical characterization of the NewReno fast recovery algorithms. We validated our models using the ns-2 simulator. Our models accurately predict the steady-state NewReno throughput for a wide range of loss rates. Based on these models, we analytically determine the preferred operating regions for each TCP variant. Our results show that the Slow-but-Steady variant is comparable to or superior to the Impatient variant in all but the most extreme scenarios for network packet loss.
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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 | 0.000 |
| Open science | 0.004 | 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 it