Comparative performance analysis of TCP-based congestion control algorithms
Why this work is in the frame
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Bibliographic record
Abstract
Congestion control is a challenging problem for us. We tried to analyse the end-to-end congestion control algorithms, i.e., TCP Tahoe, TCP Reno, TCP Newreno, TCP Veno, etc. In the literature, TCP implements a window-based flow control mechanism which leads to vary the window size within a range. Older TCP designed assuming packet loss is always inferred due to congestion on link which leads to degradation of performance in wireless networks where random loss occurred due to transmission error or noise. This well-known problem affects on TCP performance. TCP Veno has been successfully proposed to deal with random loss efficiently and its performance is discussed in the literature. This paper evaluates the complex model for different performance parameters, i.e., throughput, queuing delay, goodput, etc. In addition, we have also proposed new performance metric to measure the network performance. We also tried to study and compare the analytical results with simulated data at different levels of loss rate.
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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 | 0.001 | 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 | 0.001 | 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