Auto-Scaling Techniques for Clouds Processing Requests with Service Level Agreements
Bibliographic record
Abstract
Auto-scaling mechanisms allow applications running on Cloud environments to maintain a guaranteed Quality of Service while efficiently utilizing resources and keeping operational costs low for the service providers.However, creating such an auto-scaling framework may be challenging due to the need to precisely estimate resource usage while the workload patterns vary significantly.The research presented in this thesis focuses on automatic provisioning of compute resources in the Cloud performed by an intermediary enterprise for a single client enterprise.The enterprise hosting a broker uses techniques for dynamically controlling the number of resources used by the client enterprise.The research introduces three autoscaling techniques: a reactive, a proactive and a hybrid technique.These techniques allow resources to be scaled based on user demand.The primary goal of these auto-scaling techniques is to achieve a profit for the intermediary enterprise while maintaining the desired grade of service for the client enterprise.A secondary goal is to generate a lower cost for the client enterprise in comparison to the situation in which the client acquires resources directly from the cloud provider.The techniques support both on-demand requests as well as requests with service level agreements (SLAs).The effectiveness of the proposed auto-scaling techniques is demonstrated through experiments performed on proof of concept prototypes and simulations.The experimental results show that for a number of different combinations of system and workload parameters experimented with, the proposed algorithms lead to a significant broker profit and a lower user cost in comparison to a conventional non-autoscaling system.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".