Bibliographic record
Abstract
This thesis presents a new congestion control mechanism for TCP denominated SmoothTCP. The objective of this proposal is to have a congestion control mechanism whose performance behavior can be modified by using parameters configured externally. We particularly focus on round-trip time (RTT), fairness, and packet drops, all important performance metrics in various environments, including high-speed networks, multimedia over TCP and wireless. Therefore, we defined SmoothTCP as a subset of congestion control functions. Each one of these functions can have up to five metrics of control configured externally, namely, timeout retransmissions, fast retransmissions, Round-Trip Time (RTT), ICMP-SQ messages and ECN packets. Having defined SmoothTCP as a set of congestion control functions, we described the general behavior of some of its instances such as SmoothTCP-q, SmoothTCP-fxr, SmoothTCP-e, SmoothTCP-rq. In addition, we take a particular instance, SmoothTCP-q, and show its properties related to proactiveness, that is, its characteristics to modify the congestion window size in order to avoid packet drops. Additionally, we show the behavior of SmoothTCp-q involving various connections, particularly concerning to fairness or the capability to share the bandwidth equally among all the connections. We concluded that some instances of SmoothTCP, such as SmoothTCP-q, SmoothTCP-e and SmoothTCP-rq avoid packet drops and can control the maximum Round-Trip Time of a connection if configured correctly. Related to fairness, we concluded that certain configured features of these instances of SmoothTCP influence its fairness and show how to modify them in order to have a more equal bandwidth distribution among all the connections. Keywords. Transport Control Protocol, congestion control and avoidance, network performance, multimedia traffic, quality of service.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".