Automated Resource Dimensioning in Cloud Using Hybrid Reinforcement Learning
Bibliographic record
Abstract
Resource dimensioning refers to the process of determining the amount of resources needed to achieve target KPIs of Virtual Network Functions (VNFs) in a Network Service (NS) in a cost-effective fashion. VNFs in an NS usually have some dependency relationship among themselves. This relationship can either be represented as a chain of VNFs (in the case of Service Function Chain “SFC”), or by more general graph topologies (Virtual Network Function – Forwarding Graph “VNF-FG”). In cloud computing, a similar concept can be found in microservices whereby microservices interact with each other to implement the functionality of the application. The dependencies between the VNFs (or microservices) imply that the performance of a given VNF does not depend only on the amount of resources available to itself, but also on the performance of other VNFs on which it depends. This makes developing accurate solutions for resource dimensioning a challenging task. In this paper, we propose a Hybrid Reinforcement Learning (RL)-based solution to automatically generate resource amounts for VNFs such that they meet the expected performance of the NS with minimal resources. The proposed solution relies both on a simulation and a real cloud environment. We evaluated the performance of the proposed solution in terms of training and inferencing convergence, and inferencing time. The results demonstrate that our training and inferencing algorithms successfully converge to an optimal solution.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".