Model Driven Deployment of Auto-Scaling Services on Multiple Clouds
Bibliographic record
Abstract
Hybrid cloud platforms have been adopted to facilitate different parts of services to deliver functionalities to service consumers. Each cloud platform offers elastic resource allocation, which accommodates fluctuating demands on services by automating the provision/deprovision of resources, referred as auto-scaling. In term of service deployment, auto-scaling is usually not interoperable between multiple cloud platforms. As a result, the service level auto-scaling strategy needs to be configured separately on disparate cloud platforms, which incurs difficulties in tracing the configuration and maintaining consistent deployment. This paper presents a model-driven method to connect a cloud platform independent model of services with cloud specific operations. Through the automated transformation from model to the configuration, we use cloud management tools to deliver auto-scaling deployment across clouds. We demonstrate our method with scaling configuration and deployment of an open source benchmark application - Dell DVD store on two cloud platforms, AWS and Rackspace. The experiment demonstrates our proposed method resolves the vendor lock issues by a model-to-configuration-to-deployment automation. The empirical measurement shows our method reduces the effort of deploying auto-scaling services on cloud platforms.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".