Speakers
Description
The introduction of models for analyzing the impact of script-based algorithms in web applications on overall CPU load is becoming increasingly important in modern computing environments. However, there is a limited availability of specialized analytical tools and models addressing this problem.
In response to this gap, a model has been developed to evaluate CPU load based on key scalar performance indicators. The proposed model represents temporal and computational characteristics of code execution through measurable scalar parameters, enabling a structured assessment of processing demand.
The model has undergone optimization, resulting in improved accuracy of performance metrics, enhanced process stability, increased precision of evaluation results, and overall improved computational efficiency. Additionally, the optimized model contributes to better system responsiveness and user-perceived performance.
This paper presents an optimized approach for CPU load evaluation during the execution of code structures and script-based algorithms, offering a practical tool for performance analysis and optimization in web-based systems.