Optimizing Cloud and IoT Resource Utilization: A proactive, application-agnostic auto-scaling technique using machine learning
Bibliographic record
Abstract
The effective monitoring of resource utilization in cloud environments is crucial for ensuring compliance with Service Level Agreements (SLAs) and Quality of Service (QoS) standards, particularly given the increasing reliance on technology in modern society. While cloud providers offer different levels of resources, it is ultimately the responsibility of application providers to select the most appropriate option in terms of SLA, QoS, and cost. To accommodate the demands of unpredictable cloud applications, auto-scaling techniques are utilized. This study focuses on developing a proactive, application-agnostic auto-scaling technique that can forecast resource utilization rates for cloud applications thus facilitating the execution of auto-scaling models. To achieve this goal, the study examines the merits and drawbacks of vertical and horizontal scaling and explores various auto-scaling approaches such as reactive, proactive, and hybrid. Furthermore, it performs a comparative analysis of the performance of models integrating deep learning techniques and time series analysis, and provides insights for enhancing existing machine learning models by incorporating correlated attributes of virtual machines as exogenous attributes.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".