Joint backhaul and access optimization for service-segment-based VN admission control
Bibliographic record
Abstract
In this paper, we consider the problem of admission control of wireless virtual network (VN) service requests in a multi-service network with service-specific service function chains (SFC). SFC imposes traversal constraints on flows, i.e., each flow must visit certain service-specific nodes in a specific order. We leverage the fact that all flows within a service have common traversal constraints and propose a “service-based” admission control approach. For both of the service-based and flow-based approaches, we propose novel joint optimizations of backhaul and access networks for the admission control of VN service requests. We show that our optimization formulation is convex, hence computationally efficient and tractable. We also show that the proposed method is applicable to general backhaul and access networks. The simulation results show that the service-based approach incurs no loss in optimality while greatly reducing problem complexity compared to the flow-based counterpart.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".