Models And Methods of Analysing Infrastructure Performance in Cloud Environments Based on Process Optimisation Methods
Bibliographic record
Abstract
The study aimed to develop models and methods for analysing infrastructure performance in cloud environments that consider the complexity and dynamism of modern IT systems. The development of adaptive resource management models capable of responding to changing loads in real time was emphasised. New methods of process optimisation were developed, including the use of artificial neural networks for load forecasting and dynamic resource allocation. Solutions for efficient management of computing and storage capacities were modelled and simulated. The use of adaptive models based on neural network technologies has increased the accuracy of load forecasting by up to 95% and reduced costs by 20% through the automation of resource management. Practical experiments conducted in the Amazon Web Services (AWS) and Microsoft Azure environments confirmed the effectiveness of the approaches under various load conditions. These results help to improve the stability of cloud services, reducing the risk of overload, downtime and data loss. The proposed models are universal and can be applied in various industries, including the financial sector, e-commerce and healthcare, which allows them to effectively solve the problems faced by modern information systems. The findings of the study highlight the importance of integrating artificial intelligence into performance management, which ensures the flexibility and scalability of cloud environments. This creates new opportunities to optimise processes, improve service quality and reduce operating costs, creating the basis for further research and development in the field of 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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".