IMS network deployment cost optimization based on flow-based traffic model
Why this work is in the frame
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Bibliographic record
Abstract
The IP Multimedia Subsystem (IMS) is envisioned as the next-generation IP-based multimedia system that integrates data, speech, and video network services over both wireless and wireline networks. In this paper, we focus on optimizing the cost of SIP server deployment in an IMS network. To reflect the traffic loads on the servers, a flow-based model is used to characterize the SIP traffic. Formulated as a linear programming problem, the cost optimization involves mapping a logical IMS core network topology into a physical network topology. Three potential mapping strategies are proposed. Each strategy's specific constraints are incorporated into the mathematical formulation of the problem. A numerical example of each strategy is presented, and the discussion on the formulations is provided.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it