A Novel Transformer Reinforcement Learning-Based NFV Service Placement in MEC Networks
Bibliographic record
Abstract
The advent of 5G networks has facilitated various Industry 4.0 applications requiring stringent Quality-of-Service (QoS) demands, notably Ultra-Reliable Low-Latency Communication (URLLC). Multi-Access Edge Computing (MEC) has emerged as a key technology to support these URLLC applications by bringing computational resources closer to the user, thus reducing latency. Meanwhile, Network Function Virtualization (NFV) supports 5G networks by offering flexibility and scalability in service provisioning across various applications. Despite their benefits, MEC networks must adapt to dynamically fluctuating user demands and varying workloads, which can create challenges in maintaining QoS. This paper addresses the Virtual Network Function (VNF) placement problem in MEC networks, focusing on minimizing costs while ensuring QoS through VNF reuse. We propose a novel solution based on the Deep Transformer Q-network (DTQN) algorithm, leveraging reinforcement learning to optimize VNF placement and redeployment. Extensive simulations demonstrate that our DTQN algorithm outperforms baseline approaches, achieving up to a 9% improvement over the D3T-based method and up to 56% over the DQN-based method in terms of average rewards under specific scenarios. This results in significant improvements in cost efficiency, resource utilization, and QoS maintenance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".