A hybrid approach to automatic IaaS service selection
Bibliographic record
Abstract
Cloud computing provides on-demand resources and removes the boundaries of resources’ physical locations. By providing virtualized computing resources in an elastic manner over the internet, IaaS providers allow organizations to save upfront infrastructure costs and focus on features that discriminate their businesses. The growing number of providers makes manual selection of the most suitable configuration of IaaS resources, or IaaS services , difficult and time consuming while requiring a high level of expertise. In our previous paper we proposed QuARAM recommender, a general platform for automatic IaaS service selection. In this paper, we present in detail the hybrid approach to automatic service selection used in our platform. The selection process begins with automatic extraction of an application’s features, requirements and preferences, which are then used to produce a list of potential services for the application’s deployment. We use case-based reasoning and MCDM (Multi-criteria Decision Making) to provide a recommendation of suitable services for application deployment, clustering to handle the problem of a large search space and a service consolidation method to improve the resource utilization and decrease the total service price. We carry out a case study with a prototype implementation of our platform to demonstrate that automatic IaaS service selection using a combination of all the proposed approaches is both practical and achievable.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".