An Incremental Learning Assurance Approach for Intent Based Networking Enabled Data Centers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Intent Based Networking (IBN) is a novel paradigm that aims to create a closed-loop automation towards building a self-governed network. An inherent part of IBN is the network assurance, which tries to automatically detect any performance drift and propose a remedy solution. Following this trend, in this paper we propose a novel IBN -enabled network assurance approach in a data center setting, in order to predict key resource utilization metrics and to help network administrators to proactively take corrective actions when needed. Specifically, we propose an incremental learning approach for data center assurance, designed to accommodate the dynamic nature of infrastructures. Experimental findings obtained from real-world datasets substantiate the effectiveness of our approach, demonstrating its ability to accurately forecast resource utilization even amidst highly dynamic environments.
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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 it