Analysis of the distribution of tasks in the cloud and fog computing system

This article presents a comparative analysis of the functioning of cloud and fog computing using the FogTorch software tool. The research focuses on modeling and evaluating the performance, reliability, and efficiency of distributed computing systems. The paper describes in detail the stages of modeling, including infrastructure definition, application requirements configuration, configuration of location and routing policies, as well as the process of collecting and analyzing the results. Based on experimental data, a detailed analysis of node failure rates, system availability, data processing time, and network resource utilization was performed depending on various load factors. The results of the study show that cloud computing demonstrates higher reliability with an increase in the number of users compared to cloud systems, although it has lower performance when processing resource-intensive tasks. Special attention is paid to the analysis of the dependence of the volume of transmitted data on the number of connected devices and the influence of the geographical distribution of nodes on delays in the system. Based on the results obtained, practical recommendations are proposed for optimizing the distribution of tasks between cloud and fog nodes, improving energy efficiency, and planning for system scalability, taking into account the growing number of users. The study also includes an analysis of system performance under various load scenarios and provides specific metrics for evaluating the effectiveness of distributed computing. The results obtained can be used in the design and optimization of hybrid cloud-fog architectures, especially in the context of the development of Internet of Things technologies and industrial automation systems. The study demonstrates the importance of an integrated approach to the design of hybrid cloud-fog systems and suggests specific strategies to improve their efficiency in the face of dynamically changing loads and requirements of modern applications.