A conceptual modeling framework for internet traffic engineering problems
Bibliographic record
Abstract
We present a conceptual modeling framework for analyzing and modeling solutions to different Internet Traffic Engineering (ITE) problems involving measurement, characterization, and control of network or inter-network traffic. The framework bases itself on the concept of Clusters which can, in fact, be used to model many other network problems. Our effort is targeted towards a large- scale initiative for designing a Unified Modeling Language (UML) Profile for conceptual modeling of typical next- generation network problems. In this paper we present UML-based framework to support modeling ITE problems. The modeling framework extends UML 1.5 and is contributes towards an initiative by the authors to create a robust UML Profile for ITE (PoITE). The contribution of the work is to provide the area of ITE with basic concepts and structure for designing UML models for solving ITE problems. In brief, the area of ITE encompasses issues pertaining to the performance evaluation and performance optimization of operational IP networks. Traffic Engineering (9,10), as such, focuses on the application of technology and scientific principles to the measurement, characterization, modeling, and control of network or inter-network traffic (3). ITE is a complex area of networking that takes into account many aspects and scenarios corresponding to different environments. However, in this paper, we consider an abstraction that avoids many finer details of ITE, and instead, focuses on providing a generic framework that can support modeling ITE problems. This document is based on: • RFC3272 (Principles of Internet Traffic Engineering) (3), and
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".