## 2 State of the Art  
The recent shift to Edge Computing models has placed a greater emphasis on cheaper and more power-efficient systems that are not well addressed by x86/64 architectures. Instead, many Edge Computing solutions are turning to alternative architectures, such as ARM-based processors, which are known for their lower power consumption and cost-effectiveness. These architectures are better suited for distributed, resource-constrained environments where Edge devices operate with limited power and processing capabilities. As Edge Computing continues to grow, there is an increasing demand for customized, specialized hardware that can meet these performance and energy efficiency needs, driving innovation beyond traditional x86/64 systems. This shift is helping industries deploy intelligent applications at the Edge, closer to the source of data, with reduced latency and improved scalability. Different authors have used differing approaches to the analysis of the different options offered by virtualization and containerization for the Computing Continuum, from the Cloud to User Equipment (UE), passing through the Edge. In [9], the authors provide a systematic review of container virtualization in real-time environments in Industry 4.0, analyzing 37 papers on real-time constraints, task latency and container platforms for cyber-physical systems. Also, in [10], the authors address the need for a systematic analysis of containerization, focusing on its role as a lightweight alternative to virtual machines for deploying modern applications in diverse environments. They taxonomically classify existing research on the performance comparison between hypervisors and containers and analyze container orchestration and performance monitoring tools. In [11], the authors conduct a systematic literature review to identify the most popular container technologies and trends in their research, facilitating more informed decision-making. In [4], the authors perform a systematic mapping of research on these core technologies, revealing that most articles focus on deployment through validation and solution research, while fewer papers address orchestration, particularly in philosophical and model-related contexts. The findings highlight gaps in the literature, offering valuable insights for researchers, industries and service providers. Regarding Cloud-centered applications for these technologies, in [12], the authors compare the performance of three Cloud architectures: Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS), focusing on the differences between containerization and virtualization. The findings indicate that while containers generally perform better in SaaS and PaaS environments, the performance metrics for IaaS are nearly the same for both methods. This research aims to help users make informed decisions about their Cloud deployment strategies by providing clear, quantifiable data on these differences. Also, in [3], the authors examine the performance differences between these technologies across IaaS, PaaS and SaaS Cloud architectures. The findings indicate that containers generally offer better performance for SaaS and PaaS, while the performance for IaaS is nearly identical between the two methods, providing valuable insights for users deciding between them. In [13], the authors focus their interest on interactive training simulations, which are used across various industries and challenge traditional computing networks due to their high-performance demands. They examine how to extend traditional high-performance computing (HPC) environments into Cloud-based services to support multiple interactive simulations while maintaining performance. Their work evaluates four HPC load-balancing techniques--virtualization, software containers and clustering--to optimize the scheduling and execution of these simulations, finding that the choice of technique should be based on cluster resources, job competition and software requirements.