Analyzing auto-scaling issues in cloud environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cloud computing is becoming increasingly widespread and sophisticated. A key feature of cloud computing is elasticity, which allows the provisioning and de-provisioning of computing resources on demand, via auto-scaling. Auto-scaling techniques are diverse, and involve various components at the infrastructure, platform and software levels. Auto-scaling also overlaps with other quality attributes, thereby contributing to service level agreements, and often applies modeling and control techniques to make the auto-scaling process adaptive. A study of auto-scaling architectures, existing techniques and open issues provides a comprehensive understanding to identify future research solutions. In this paper, we present a survey that explores definitions of related concepts of auto-scaling and a taxonomy of auto-scaling techniques. Based on the survey results, we then outline open issues and future research directions for this important subject in cloud computing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it