A Model for Steady State Throughput of TCP CUBIC
Bibliographic record
Abstract
For transmission control protocol (TCP), CUBIC is a TCP-friendly high-speed variant, in which the window size is a cubic function of time since the last loss event. TCP CUBIC is implemented in Linux operating systems and performs well in wired networks with large bandwidth-delay product. Most of the evaluations of TCP CUBIC are conducted via simulations or experiments. Analytical models for TCP CUBIC are few. In this paper, we propose a Markovian model to determine the steady state throughput of TCP CUBIC in wireless environment. The proposed model considers both congestion loss and random packet loss due to fading. We derive the stationary distribution of the Markov chain and obtain the average throughput based on the stationary distribution. Simulations are carried out to validate the analytical model. Results show that the simulated stationary distribution and the average throughput are both very close to our analytical results. Furthermore, we analyze the throughput performance of TCP CUBIC. Results show that random packet loss reduces the normalized average throughput more for end-to-end flow with large bandwidth-delay product. We propose an improvement to increase the throughput performance of TCP CUBIC by moderately increasing the window growth factor and the multiplicative decrease factor.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".