Network Assurance in Intent-Based Networking Data Centers with Machine Learning Techniques
Bibliographic record
Abstract
Intent-Based Networking (IBN) is a recently proposed networking solution that allows networks to be configured and adapted autonomously according to the users' or operators' high-level intentions. However, a significant component of IBN is to assure that the network accurately and automatically deploys the intent throughout its lifecycle. To this end, in this study, we propose a network assurance solution for data center IBN networks. For the assurance model, we propose some specific data preparation procedures and Machine Learning (ML) models for the problem of time series forecasting. Specifically, we construct three main ML models that are based on the architecture of Convolutional Neural Networks (CNN) and Recurrent Neural Network (RNN). Our evaluation experiments, based on real data center Virtual Machine (VM) data traces, reveal the effectiveness of our methods in terms of CPU percentage usage prediction accuracy and speed. At the same time, our best-performing model can predict sufficiently far into the future with good accuracy.
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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 | 0.001 | 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".