Using TCP models to understand bandwidth assurance in a Differentiated Services network
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, a comprehensive analytical model to predict the bandwidth achieved by aggregates of TCP flows in a DiffServ network is presented. The model predicts achieved bandwidth in three different cases: an over-provisioned network, an under-provisioned network, and a near-provisioned network. In developing the model, we ensure that all parameters are measurable using standard tools and information available from routers and network management tools in today's networks. Simulation was used to establish the validity of the model and understand its scope of applicability and limitations. Using the model, we explain why achieved excess bandwidth is based on factors such as RTT, packet size, and CIR. Finally, we present a novel extension of the model to predict the bandwidth of TCP flows in a Diffserv network with multiple congested nodes.
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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 | 0.001 |
| 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