Towards optimal synchronization in NFV‐based environments
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Network Function Virtualization (NFV) is known for its ability to reduce deployment costs and improve the flexibility and scalability of network functions. Due to processing capacity limitations, the infrastructure provider may need to instantiate multiple instances of the same network function. However, most of network functions are stateful, meaning that the instances of the same function need to keep a common state and hence the need for synchronization among them. In this paper, we address this problem with the goal of identifying the optimal synchronization pattern between the instances in order to minimize the synchronization costs and delay. We propose a novel network function named Synchronization Function able to carry out data collection and further minimize these costs. We first mathematically model this problem as an integer linear program that finds the optimal synchronization pattern and the optimal placement and number of synchronization functions that minimize synchronization costs and ensure a bounded synchronization delay. We also put forward three greedy algorithms to cope with large‐scale scenarios of the problem, and we explore the possibility to migrate network function instances to further reduce costs. Extensive simulations show that the proposed algorithms efficiently find near‐optimal solutions with minimal computation time and provide better results compared to existing solutions.
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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 | 0.001 | 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 | 0.001 |
| 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