The Impact of Database Layer on Auto-Scaling Decisions in a 3-Tier Web Services Cloud Resource Provisioning
Bibliographic record
Abstract
This paper investigates the impact of the database layer on the scaling actions of the business layer of a 3-tier web service system in cloud resource provisioning. The research question is "What is the impact of the database layer on the business layer auto-scaling decisions?" In this work two hypotheses are tested: 1) "Database tier capacity has no effect on the business tier scaling decisions" and 2) "Scaling up of a database tier increases Service Level Agreement (SLA) violations." To test the hypotheses, an auto-scaling simulation package based on Queuing Network Models (QNM) and Layered Queuing Network Models (LQNM) is developed. The auto-scaling simulation package is used to investigate the database impact on the business tier scaling decisions in the cloud environments with three different workload patterns (growing, periodic, and unpredictable patterns). This paper also provides an analytical investigation that empirically validate the hypotheses. The results suggest that the database tier has no effect on the business tier scaling decisions. However, decreasing the capacity of the database layer increases the rate of the 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".