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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".