Dynamic Topology Design of NFV-Enabled Services Using Deep Reinforcement Learning
Bibliographic record
Abstract
Next-generation networks are endowed with enhanced capabilities thanks to software-defined networking and network function virtualization (NFV). There is a radical shift from device-centric to experience-driven environments of which data is the primary driver behind its running engines. In this paper, we consider joint topology design, traffic routing and NF placement for unicast NFV-enabled services. We develop an end-to-end model-free deep reinforcement learning (RL) framework to dynamically allocate processing and transmission resources, while considering time-varying network traffic patterns. First, we provide a flexible pre-processing technique that represents and reduces the state space and action space of the considered joint problem for the deep RL algorithm. Second, we present a deep deterministic policy gradient (DDPG) algorithm that is enhanced with a model-assisted exploration procedure. Due to the multiple resource types with strongly adverse effects, the existing vanilla DDPG algorithm cannot achieve consistent performance. The model-assisted exploration procedure, which utilizes a perturbed step-wise sub-optimal integer linear program, bootstraps and stabilizes the vanilla DDPG algorithm and finds optimal solutions efficiently.
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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 | 0.003 |
| 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) | 0.002 | 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".