Intent-Based Management for Network Automation
Bibliographic record
Abstract
The complexity of modern networks, combined with business requirements and human oversight, presents significant management challenges. This thesis focuses on developing a holistic management system, Emergence, which integrates intent-based networking, large language models (LLMs), and policy-driven automation through closed control loops. Our contributions are: 1) Formalizing intents into a hierarchy of policies at various abstraction levels; 2) Intelligent and automated intent-to-policy decomposition using generic pre-trained LLMs, resulting in a Policy Tree—an ordered set of Monitor-Analyze-Plan-Execute (MAPE-K) policies; 3) Automated and scalable intent deployment through control loops and Finite State Machines for policy execution; 4) Monitoring and mitigating intent drift using LLMs and additional tools to assure intents in response to changing conditions. Our solution provides a robust, scalable, and intelligent system for modern network management, fulfilling and assuring intents 1-3x faster on average compared to manual procedures. Moreover, the policy-based approach enhances explainability and control over management decisions and actions. Lastly, we share future directions towards trustworthiness.
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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".