Profit-driven resource provisioning in NFV-based environments
Bibliographic record
Abstract
Network Function Virtualization (NFV) is an emergent paradigm that is currently transforming the way network services are provisioned and managed. The main idea of NFV is to decouple network functions from the hardware running them. This allows to reduce deployment costs and further improve the flexibility and the scalability of network services. Despite these benefits, a major challenge cloud providers are still facing is how to efficiently allocate resources for NFV-based services in a way that reduces operational costs and maximizes their profits. In this paper, we address this particular challenge and propose an effective profit-driven service chain provisioning scheme designed for large-scale infrastructures spanning different geographically-distributed sites. We hence propose three algorithms that maximize the provider's profit taking into consideration energy consumption of the infrastructure and the variability of energy prices in different locations. Through extensive simulations, we show that these algorithms are able to efficiently find near-optimal resource allocations and maximize the provider's profit with minimal computational complexity.
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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 | 0.001 | 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".