Bibliographic record
Abstract
Change management in a cloud environment is often complicated by the different needs of the cloud clients. Changes are not applied all at once. For example, a client may require that a change to the Platform-as-Service (PaaS) instance assigned to it must only be done on the weekend while another client allows for the change to be done at any time. The time periods at which changes can be applied may be specified in SLAs. A change deployment schedule for making changes to PaaS instances often depends on the cloud provider policies and on the SLAs between the clients and the cloud provider. Different sets of cloud provider policies may result in different deployment schedules. Changes are not always successful. This may result in a change being unsuccessful and a return to a previous state in order to re-start the change. Neither is desirable since it may be impact SLA guarantees such as service availability or service time that could result in the cloud provider paying out penalties. Since changes are not all applied at once it may be desirable to modify the change deployment schedule. For example, if an operator is not highly skilled or if the change's complexity is higher than expected then it may be preferable to apply the change during a time period when there are relatively few customers in order to minimize SLA violations. This paper shows how strategy trees can be incorporated into an autonomic change management system that could result in a switch of cloud provider policy sets to determine a new deployment schedule on the fly. Our experiments show that this approach can save time while minimizing SLA violations.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".