Cost-Efficient Mapping for Fault-Tolerant Virtual Networks
Why this work is in the frame
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Bibliographic record
Abstract
Network virtualization provides more flexibility in network provisioning as it offers physical infrastructure providers (PIP) the possibility of smoothly rolling out many separate networks on top of an existing infrastructure. A major challenge is the embedding problem of mapping virtual networks (VNs) onto PIP infrastructure. In the literature, a good deal of research has focused on providing heuristic approaches to this NP-hard problem, usually with the assumption that the PIP infrastructure is operational at all times. In virtualization environment, a single physical node/link failure can result in one or more logical link failures as it effects all VNs with a mapping that spans over. Setting up a dedicated backup for each VN embedding that is not shared with others is an inefficient use of resources. To address these concerns, this paper proposes two classes of periodic VN protection against link and node failures: (a) in the physical layer, by using a path or segment <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$p$</tex> <mathgraphic fileref="jarray-ieq1-2295612.gif" graphicformat="GIF"/></formula> -cycle technique and a column generation optimization model, and (b) in the VN layer, by augmenting the topology with redundant resources and subsequently applying a column generation mapping model. Our simulations show a clear advantage of our approaches over benchmarks in terms of PIP profit, backup cost/rate and resource use.
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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 | 0.001 | 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 it