This PhD thesis presents a conclusion of a six-year-long research in the field of Cloud Computing, Fog Computing, and Internet of Things. The main goal of the intercooperation of such domains, which are often associated with the Cloud-to-Thing Continuum is to process, store, and analyse vast amount of data of IoT applications in an effective way. The latest complex distributed systems involving thousands of IoT devices promote widely usable services by leveraging the computing and storing capacities of cloud data centres. To enhance the elasticity of a concrete service, cloud resources are often aided by resource-constrained fog nodes to improve the response time of the IoT application and to disperse the various types and unforeseen amounts of data. These IoT-Fog-Cloud systems require significant investments in terms of design, development and operation, therefore, the use of simulators for their investigation is inevitable. There are a large number of simulators addressing the analysis of parts of these systems, however, it is obvious that only a state-of-the-art simulator is capable of modelling complex architectures in a realistic way, which meets modern challenges. This PhD thesis consists of three theses separated into three major chapters. The first chapter presents a detailed survey and taxonomy of various IoT, cloud, and fog simulators in order to determine the key requirements of a compact and well-defined IoT-Fog-Cloud simulator. Furthermore, it presents an in-depth analysis and a comparison of two major fog simulators. The second chapter introduces the IoT and the pricing extension, exploiting a multi-cloud environment with resource allocation strategies in the DISSECT-CF-IoT simulator. Finally, in the third chapter, the DISSECT-CF-Fog simulator is presented which is able to model a multi-layered fog topology with energy measurement, task allocation algorithms and, mobility and actuator events.