Supporting service differentiation through end-to-end QoS routing
Bibliographic record
Abstract
Providing better than best-effort service in the current Internet paradigm requires support from the underlying network infrastructure. The IntServ (integrated services) model was proposed for guaranteeing per-flow, end-to-end quality of service (QoS). However, IntServ model is too restrictive for large-scale deployment for providing QoS in the Internet due to its scalability problem. Faced with this problem, the DiffServ (differentiated services) model has been proposed, and now is becoming the preferred solution. DiffServ aims to resolve the scalability problem existing in the IntServ. Based on the idea of traffic aggregation, DiffServ intends to be a scalable, flexible approach for supporting multiple levels of service. However, the DiffServ model has not provided sufficient mechanisms to efficiently manage network resources and effectively control traffic admission into core networks. In this paper, we apply the seminal concepts of DiffServ and bandwidth brokers (BB) to design the end-to-end SiMO (single service multiple options) routing framework. The goals are three-fold, namely (1) supporting SiMO service differentiation through QoS routing; (2) combining resources management and admission control with QoS routing; and (3) enhancing the end-to-end performance guarantee to per aggregate class. Through extensive simulation using two kinds of applications, i.e., IP telephony and MPEG streams, we have demonstrated that the SiMO routing framework is capable of providing QoS service by supplying paths with different end-to-end delay characteristics to per aggregate classes.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".