Development and research of simulation models of cloud and fog computing in the Fogtorch program

In recent years, there has been a growing interest in distributed computing, especially in the context of the Internet of Things, where fast data processing and transmission with minimal delays are required. One of the promising solutions is the use of foggy computing, which allows you to distribute computing tasks between devices located closer to the data source. However, designing efficient and reliable such systems requires in-depth analysis of their performance in various scenarios. This requires modeling tools such as FogTorch, which allow you to study the influence of network parameters, distribution of computing resources and topology of the system on its characteristics. The relevance lies in the need to evaluate the performance of distributed systems for real-world applications of the Internet of Things. The goal is to develop and research simulation models of cloud and fog computing aimed at analyzing the performance of distributed systems, taking into account factors such as data transmission delays, network bandwidth and resource allocation optimization, offering methods for evaluating the effectiveness of interaction between cloud and fog nodes in Internet of Things systems, as well as studying their adaptation to various operating scenarios. Data on the performance of cloud and fog computing in various scenarios have been obtained. Key parameters such as data transmission delays, network bandwidth and efficient allocation of computing resources between nodes are evaluated. It is proved that optimizing the distribution of tasks and adapting the network taking into account its characteristics can significantly reduce delays and increase the energy efficiency of the system. It is revealed that the integration of cloud and fog computing allows to improve the quality of service in conditions of changing loads. Development of effective methods and models for optimizing distributed computing in Internet of Things systems using cloud and fog technologies. The results can be used to improve the architecture of such systems, which will lead to reduced delays, increased energy efficiency and improved service quality. Models can be applied to design and configure real IoT systems, providing higher performance.