Optimizing Resource Fragmentation in Virtual Network Function Placement Using Deep Reinforcement Learning
Bibliographic record
Abstract
In the 6G wireless era, the strategical deployment of Virtual Network Functions (VNFs) within a network infrastructure that optimizes resource utilization while fulfilling performance criteria is critical for successfully implementing the Network Function Virtualization (NFV) paradigm across the Edge-to-Cloud continuum. This is especially prominent when resource fragmentation –where available resources become isolated and underutilized– becomes an issue due to the frequent reallocations of VNFs. However, traditional optimization methods often struggle to deal with the dynamic and complex nature of the VNF placement problem when fragmentation is considered. This study proposes a novel online VNF placement approach for Edge/Cloud infrastructures that utilizes Deep Reinforcement Learning (DRL) and Reward Constrained Policy Optimization (RCPO) to address this problem. We combine DRL’s adaptability with RCPO’s constraint incorporation capabilities to ensure that the learned policies satisfy the performance and resource constraints while minimizing resource fragmentation. Specifically, the VNF placement problem is first formulated as an offline-constrained optimization problem, and then we devise an online solver using Neural Combinatorial Optimization (NCO). Our method incorporates a metric called Resource Fragmentation Degree (RFD) to quantify fragmentation in the network. Using this metric and RCPO, our NCO agent is trained to make intelligent placement decisions that reduce fragmentation and optimize resource utilization. An error correction heuristic complements the robustness of the proposed framework. Through extensive testing in a simulated environment, the proposed approach is shown to outperform state-of-the-art VNF placement techniques when it comes to minimizing resource fragmentation under constraint satisfaction guarantees.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 |
| 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".