An Automated VNF Manager based on Parameterized Action MDP and Reinforcement Learning
Bibliographic record
Abstract
Managing and orchestrating the behaviour of virtual network functions (VNFs) remains a major challenge due to their heterogeneity and the ever-increasing resource demands of the served flows. In this paper, we propose a novel VNF manager (VNFM) architecture to automate the process of selecting appropriate VNF management actions (e.g., migration and vertical and horizontal scaling) with their corresponding configuration parameters (e.g., migration location or amount of resources needed for scaling). More precisely, we first propose a novel Markov decision process with parameterized actions to accurately describe each VNF and its permissible lifecycle management (LCM) operations. The use of parameterized actions allows us to rigorously represent the functionalities of the VNFM in order perform various operations on the VNFs. Next, we propose a two-stage reinforcement learning (RL) scheme that alternates between learning optimal LCM actions and updating their parameters selection policy. In contrast to existing schemes, the proposed work uniquely provides a holistic management platform that unifies individual efforts targeting single LCM functions such as VNF placement and scaling. Performance evaluation results demonstrate the efficiency of the proposed VNFM in maintaining the required performance level of the VNF while optimizing its resource configurations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".