SLA Management in Intent-Driven Service Management Systems: A Taxonomy and Future Directions
Bibliographic record
Abstract
Traditional, slow and error-prone human-driven methods to configure and manage Internet service requests are proving unsatisfactory. This is due to an increase in Internet applications with stringent quality of service (QoS) requirements. Which demands faster and fault-free service deployment with minimal or without human intervention. With this aim, intent-driven service management (IDSM) has emerged, where users express their service level agreement (SLA) requirements in a declarative manner as intents . With the help of closed control-loop operations, IDSM performs service configurations and deployments, autonomously to fulfill the intents. This results in a faster deployment of services and reduction in configuration errors caused by manual operations, which in turn reduces the SLA violations. This article is an attempt to provide a systematic review of How the IDSM systems manage and fulfill the SLA requirements specified as intents. As an outcome, the review identifies four intent management activities, which are performed in a closed-loop manner. For each activity, a taxonomy is proposed and used to compare the existing techniques for SLA management in IDSM systems. A critical analysis of all the considered research articles in the review and future research directions are presented in the conclusion.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".