Bandwidth assurance issues for TCP flows in a differentiated services network
Why this work is in the frame
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Bibliographic record
Abstract
Much industry attention has been focused on providing differentiated levels of service to users on IP networks. One such proposal is the RIO scheme proposed by Clark (see ACM Transactions on Networking, 1998 ). RIO is an extension of the RED algorithm that relies on a differentiated drop treatment during congestion to cause different levels of service. The end result of differentiated dropping of packets during congestion is differentiated throughput rates for end-users. The IETF's Diffserv Working Group has recently standardized a PHB (per hop behaviour) that is based on a differentiated drop scheme-assured forwarding (AF). This paper raises issues with providing bandwidth assurance for TCP flows in a RIO-enabled differentiated services network. The main contribution is a detailed experimental study of five different factors that impact throughput assurances for TCP and UDP flows in such a network. Our study demonstrates that these factors can cause different throughput rates for end-users in spite of having contracted identical service agreements.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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