Cost and Availability-Aware VNF Selection and Placement for Network Services in NFV
Bibliographic record
Abstract
Network Function Virtualization (NFV) is a growing computing paradigm for rapid and economical provisioning of networking services (NSs). In NFV, NS is provided through a set of Virtual Network Functions (VNFs) that are hosted on the underlying infrastructure offered by the service provider. Quality of the NS such as availability, as well as the overall cost of providing the NS in NFV domain are raised as concern issues especially their objectives go against each other. This paper tackles two main problems that are directly related to the cost and availability of the NS, VNFs configuration types selection problem and VNF placement problem. Therefore, we propose two Mixed Integer Linear Programming (MILP) optimization models to address both problems and find solutions. We build a proof of concept to evaluate our proposed solutions and compare them with existing solutions from the literature. The results show that our proposed solutions can reduce the overall cost of requested NSs without violating their availability requirements.
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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 | 0.000 |
| 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".