Towards a Self-Driving Management System for the Automated Realization of Intents
Bibliographic record
Abstract
Network management faces the interrelated challenges of increasing network complexity, meeting sophisticated business requirements, and being subject to human oversight. Self-driving networks possess the key properties to overcome such challenges. We present and implement a management system that addresses several elements of a self-driving network. Our system leverages intents, a policy-based paradigm and autonomic control loops. Intent-based networking allows us to formalize how an intent can be provided as input to a control loop, and how the complexities can be abstracted from the user. To realize and assure the intent, autonomic networking enables us to create Monitor-Analyze-Plan-Execute (MAPE) loops. Finally, we execute the control loops using a policy-based approach. We propose a policy abstraction to support requirements at different levels of abstraction, and an Application Programming Interface (API) layer to reduce management complexity from the user perspective. We propose a formal policy information model to model policies across layers of abstractions and to support simplified mapping and strong consistencies among various policy abstraction levels. We have implemented our proposal and present a proof-of-concept use-case to showcase the intent refinement.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".