Resource Allocation for Blockchain-Enabled Distributed Network Function Virtualization (NFV) with Mobile Edge Cloud (MEC)
Bibliographic record
Abstract
Mobile Edge Cloud (MEC) has emerged as a promising paradigm shift from the centralized mobile cloud due to the explosive growth of edge devices and traffic volumes. Network Function Virtualization (NFV) is a key technology for managing and orchestrating the virtualized instances in MEC. However, it is challenging to perform efficient resource allocation in distributed NFV with MEC due to the multiple existence of NFV Management and Orchestration (MANO) systems in distributed NFV. In this work, we propose a blockchain-enabled NFV framework to reach consensus among multiple MANO systems for complex MEC scenarios. Moreover, we take both the latency of services and operational cost into consideration to achieve better resource allocation. Then, we formulate the efficient resource allocation for services in blockchain-enabled NFV with MEC as a multi-objective optimization problem. Due to the fact that it is difficult to solve this multi-objective optimization problem by traditional methods, we propose a dueling deep reinforcement learning approach. Simulation results are presented to show the effectiveness of our proposed scheme.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".