Intent Negotiation Framework for Intent-Driven Service Management
Bibliographic record
Abstract
To automate network operations and deployment of compute services, intent-driven service management (IDSM) is essential. It enables network users to express their service requirements in a declarative manner as intents. To fulfill the intents, closed control-loop operations carry out required configurations and deployments without human intervention. Despite the fact that intents are fulfilled automatically, conflicts may arise between user's and service provider's intents due to limited resources availability. 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 systems are still in their infancy. The available solutions are platform dependent which poses various challenges in their adoption to diverse platforms. The main focus of this work is to draft and evaluate a comprehensive and generic intent negotiation framework which can be used to develop intent negotiation solutions for diverse IDSM platforms. In this work, we have identified and defined various processes that are necessary for intent negotiation. Furthermore, a generic intent negotiation framework is presented representing interactions among the identified processes, while conflicting actors engage in the intent negotiation. The results demonstrated that the proposed intent negotiation framework increases the intent acceptance rate by up to 38 percent with processing overheads less than 10 percent.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".