Checking Service Instance Protection for AMF Configurations
Bibliographic record
Abstract
An AMF configuration is a logical organization of resources, components and service units (SUs) grouped into service groups (SGs), for providing and protecting services defined as service instances (SIs). The assignment of SIs to SUs is a runtime operation performed by the availability management framework (AMF) implementation. However, ensuring the capability of the provisioning and the protection of the SIs by the configured resources is a configuration issue. In other words, a configuration is valid if and only if it is capable of providing and protecting the services as required and according to the specified redundancy model. Ensuring this may require the exploration of all possible SI-SU assignments and in some cases different combinations of SIs, a complex procedure in most redundancy models defined in the AMF standard specification. In this paper, we explore the problem of SI protection at configuration time; we investigate and discuss its complexity and identify some special and more tractable cases.
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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 | 0.019 | 0.194 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.019 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".