Bandwidth assurance issues for TCP flows in a differentiated services network
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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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".