QoS Performance Analysis in Deployment of DiffServ-aware MPLS Traffic Engineering
Bibliographic record
Abstract
It is a trend that the integrated voice, video and data will be transported in the converged IP/MPLS core network. The combined use of the differentiated services (DiffServ) and multiprotocol label switching (MPLS) technologies is envisioned to provide guaranteed quality of service (QoS). However, such a scheme only dictates per hop behavior (PHB) and it does not control the end to end path the traffic is taking. If some link of the path is congested, packets will be dropped and QoS can not be guaranteed. Another attractive application of MPLS is for Traffic Engineering (TE), which sets up end to end routing path before forwarding data. Unfortunately, MPLS TE only reserves resource in one aggregated class, so that it can not provide QoS for differentiated services. MPLS DiffServ-aware TE makes MPLS TE aware of QoS, by combining the functionalities of both DiffServ and TE. In this paper, the QoS performance is analyzed for different type of services including VoIP, Real time Video, and best effort data traffic. The results show that the guaranteed bandwidth service can give better QoS for real time traffic such as VoIP, but worse QoS for the variable video traffic.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".