Intent Negotiation Framework for Intent-driven Service Management
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
To automate network operations and compute ser- vices, intent-driven service management (IDSM) is essential. It enables network users to express their service requirements in a declarative manner as i ntents. To fulfill the intents, closed control-loop operations perform required configurations and deployments without human intervention. Despite the fact that the intents are fulfilled automatically, conflicts may arise between users and service providers due to limited capabilities of service providers and user requirements specified as intents. This triggers IDSM system to initialize an intent negotiation process among conflicting actors. Intent negotiation involves generating one or more alternate intents based on the current state of the underlying physical/virtual resources, which are then presented to the intent creator for acceptance or rejection. In this way, the quality of services (QoS) can be improved significantly by maximizing the acceptance rate of service requests in the scenario of limited resources. However, intent negotiation sub-systems are still in their infancy. The available solutions are platform dependent which pose various challenges in their adoption to diverse platforms. The main focus of this work has been to draft a comprehensive and generic intent negotiation framework which can be used across diverse IDSM platforms. In this work, we have identified and defined various processes that are necessary for a comprehensive intent negotiation framework. A generic intent negotiation framework is then presented incorporating all the interactions among all the identified processes while conflicting actors engage in the intent negotiation, towards the fulfilment of the given service.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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