An Approach for the Automation of IaaS Cloud Upgrade
Bibliographic record
Abstract
An Infrastructure as a Service (IaaS) cloud provider is committed to each tenant by a service level agreement (SLA) which indicates the terms of commitment, e.g. the level of availability of the IaaS cloud service.The different resources providing this IaaS cloud service may need to be upgraded several times throughout their life-cycle; and these upgrades may affect the service delivered by the IaaS layer. This may violate the SLAs towards the tenants and result in penalty as they impact the tenant services relying on the IaaS.Therefore, it is important to handle upgrades properly with respect to the SLAs.The upgrade of IaaS cloud systems inherits all the challenges of clustered systems and faces other, cloud specific challenges, such as size and dynamicity due to elasticity.In this paper, we propose a novel approach to automatically upgrade an IaaS cloud system under SLA constraints such as availability and elasticity.In this approach, the upgrade methods and actions appropriate for each upgrade request are identified, scheduled, and applied automatically in an iterative manner based on the vendors descriptions of the infrastructure components, the tenant SLAs, and the status of the system. The proposed approach allows new upgrade requests during ongoing upgrades, which makes it suitable for continuous delivery.In addition, it also handles failures of upgrade actions through localized retry and undo operations automatically.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".