An IoT-Aware VNF Placement Proof of Concept in a Hybrid Edge-Cloud Smart City Environment
Bibliographic record
Abstract
Internet of Things (IoT) along with Virtualized Network Function (VNFs) are creating a wide variety of opportunities for emerging vertical applications. Network operators are faced with a strategic puzzle on how to balance limited resource availability, dynamic IoT traffic requirements, and dynamic IoT device behavior in an end-to-end communication paradigm. To this end, this paper formally defines the IoT-aware VNF Placement (IVP) problem. We then evaluate an indicative set of placement algorithms with different objective functions under static and dynamic traffic scenarios to study their impact on the overall performance. The algorithms are evaluated based on realistic IoT traffic statistics in a smart-city environment and are presented as a simulation-based case study. Evaluation results emphasize the critical impact of considering multi-objective algorithms to accurately capture a set of conflicting goals, while efficiently balancing between them when solving the IVP problem. Finally, we shed light on the importance of using sophisticated lightweight approximation algorithms, to alleviate the inadequacies of the optimal mathematical solution.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".