Using AHP/TOPSIS with cost and robustness criteria for virtual network node assignment
Bibliographic record
Abstract
In future Internet architectures, Virtual Network Providers (VNP) must be able to compose virtual networks in a way that balances security with other priorities as expressed by Service Providers (SP). In this paper, we outline a framework in which a VNP can assess the security and assurance (robustness) properties of virtual networks and use these to help select an SP-appropriate topology. The topology selection algorithm is based on the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and uses cost and robustness as selection constraints. Our framework allows the VNP to identify the customer's needs for security and balance these with other priorities such as quality of service and cost. We show that this approach fits naturally into the business model of the VNP and takes advantage of risk management activities that may already be performed by SPs. Because security concerns can dampen enthusiasm for new ways of doing business, addressing the challenges above can help ease the transition to the future Internet.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 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".