Multi-provider service negotiation and contracting in network virtualization
Bibliographic record
Abstract
Network virtualization environment (VNE) affords great business flexibility to the customers and the providers as multiple providers can jointly support a customer's virtual network. Under the current network model, a group of Infrastructure Providers (InPs) peer with each other to provide a packaged deal. Such a business arrangement is not customer-driven, does not promote fair market competition and does not ensure cost minimization. Furthermore, the on-demand nature of virtual networks requires efficient and automated service negotiation and contracting. In this paper, we present V-Mart. To the InPs, V-Mart offers an environment to participate in a faithful and fair competition over the VN resources; and to the SPs, it offers a customer-driven virtual resource partitioning and contracting engine. V-Mart uses a two-stage Vickrey auction model that is strategy-proof, flexible to diverse InP pricing models, and functions over heterogenous multi-commodity market that characterizes the NVE. Through analysis and simulation we show the flexibility and effectiveness of V-Mart.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".