Deep learning based resource forecasting for 5G core network scaling in Kubernetes environment
Bibliographic record
Abstract
5G networks are moving towards cloudification which gives the telecom operators the flexibility to manage their networks efficiently and cost-effectively. Scaling network functions on demand is one of the advantages of using container-based deployment in cloud environments. With the continuously changing network traffic patterns due to the emerging new 5G use cases, there is a need for novel automated network resources management approach in cloud-native environments. Considering the scale and the complexity of the 5G network, managing resources is a challenge. To address this, we propose a deep learning-based resource usage forecasting approach that provides useful insights for decision-making in containerized Network Function (CNF) scaling for the Kubernetes environment. Kubernetes is a container orchestration tool that becoming popular among Telecom operators due to its simplicity. We implemented a testbed in the Kubernetes environment to generate a dataset closer to real-world data for deep learning model training and evaluated the best-performing model for resource usage forecasting. We benchmarked our approach against another deep learning-based resource usage forecasting approach which proved our method can provide a highly accurate forecast for further horizons.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".