Concurrent Multipath Transfer Using SCTP: Modelling and Congestion Window Management
Bibliographic record
Abstract
Concurrent multipath transfer (CMT) using the stream control transmission protocol (SCTP) can exploit multihomed devices to enhance data communications. While SCTP is a new transport layer protocol supporting multihomed end-points, CMT provides a framework so that transport layer resources are used efficiently and effectively when sending to the same destination with multiple IP addresses. In this paper, we present two techniques for modelling the expected throughput of a CMT session; while one is based on renewal theory, the other uses a Markov chain. As far as we know, ours is the first paper to model CMT whilst considering practical transport layer resources like a shared receive buffer (RBUF). A comparison of the models showed the Markov chain to be more accurate, but suffered from scalability issues. Alternatively, the renewal model was more cost effective, but also less accurate. We also applied our models to a new problem called congestion window management, where the size of each congestion window is reconfigured for optimal performance. Again, we compared two approaches: a dynamic method that makes decisions based on instantaneous throughput, and a static method that uses an integer linear program (ILP) to generate a global solution. Results showed the static method outperforming the dynamic approach by as much as 12 percent.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".