Performance Analysis of Infrastructure Service Provision with GMPLS-Based Traffic Engineering
Bibliographic record
Abstract
Dynamic sharing of the common physical network is envisioned as a key enabler for the emerging Internet technologies. This paper addresses challenges related to resource sharing in the physical layer and analyzes the performance of infrastructure service provision with control plane mechanisms based on generalized multi protocol label switching (GMPLS). In our approach, the provisioning of infrastructure services is supported by two novel concepts for GMPLS traffic engineering (TE): resource visibility and inter-domain exchange. Resource visibility is a new network control plane concept, which defines the usage polices for transmission, multiplexing, and switching resources in multiple GMPLS layers. In our architecture, every network resource may exhibit different visibility to different services at different layers. The inter-domain exchange, here referred to as GMPLS exchange point (GXP), is the physical layer equivalent of the Internet exchange point (IXP). Just as how the IXP manages interconnections of autonomous systems (AS) in the Internet, the GXP manages dynamic interconnections of multiple provider domains and enables them to advertise their physical resources to other domains. We model the dynamic provisioning of infrastructure services using graph theory and deploy GMPLS traffic engineering (TE) to optimize the routing and resource yields. The results obtained demonstrate that traffic engineering with resource visibility and GXP brings significant performance benefits in resource utilization and infrastructure extensibility, especially when network providers set up LSPs as a result of collaborative and carrier-neutral traffic engineering where they share information about resource capabilities and utilization
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".