## 1 Introduction  
As digital transformation accelerates across various sectors, the adoption of advanced technologies such as e-Government, Industry 4.0, Agriculture 5.0 and Smart Cities is becoming increasingly widespread. These technologies, powered by the development of artificial intelligence (AI) and other emerging tools, offer unprecedented opportunities for institutions, governments and organizations to enhance efficiency, reduce operational costs, and achieve higher levels of automation [1]. The shift towards these technologies necessitates a critical examination of the underlying infrastructure, particularly in how software applications are deployed and managed to meet the growing demands of modern systems. In the context of the Computing Continuum, which spans from Cloud to Edge/Fog environments, the deployment of applications requires a careful selection of the appropriate hosting environment [2]. The decision between virtualization and containerization, two prevalent technologies in this space, plays a pivotal role in determining the performance, scalability and efficiency of these deployments [3, 4]. Virtualization, a well-established technology, allows multiple operating systems to run concurrently on a single physical machine by abstracting the hardware layer. In contrast, containerization offers a more lightweight approach by isolating applications within containers that share the host operating system's kernel, leading to faster deployment times and more efficient resource utilization. As organizations increasingly shift towards Edge Computing--where data processing occurs closer to the source rather than in centralized cloud servers--the choice between virtualization and containerization becomes even more critical. Edge environments often come with constrained resources and specific Quality of Service (QoS) requirements, making it imperative to choose the right deployment strategy [5]. Virtual machines (VMs) provide robust isolation and security, which are essential in many Edge applications. However, they can also introduce significant overhead, potentially impacting performance in resource-limited settings. On the other hand, containers, while more resource-efficient, may pose challenges related to security and orchestration in diverse and distributed Edge environments [6]. The complexities involved in deploying applications across the Computing Continuum highlight the need for a comprehensive comparative analysis of virtualization and containerization technologies [2]. This paper aims to address this need by evaluating various virtualization and containerization stacks to determine the optimal environment for hosting deployments and the applications they support. The analysis will consider key factors such as performance, scalability, security and ease of management, which are crucial for meeting the unique demands of Edge Computing. In addition to the technical evaluation, this study will also explore the broader implications of these technologies for the future of application deployment in Edge environments. As the Edge becomes an increasingly vital part of the Computing Continuum, understanding the tradeoffs between virtualization and containerization will be essential for organizations seeking to leverage these technologies effectively. Ultimately, the findings of this research will contribute to the ongoing discourse on the best practices for deploying applications in the Computing Continuum, with a particular focus on Edge Computing. As the digital landscape continues to evolve, the ability to make informed decisions about deployment strategies will be key to unlocking the full potential of emerging technologies and ensuring their successful integration into the fabric of modern infrastructure. Another possible research motivation in the study of the comparison between virtualization and containerization concerns the architecture of the CPU within the context of Edge Computing. Depending on the use of Intel (x86_64) or ARM architectures [7], the set of instructions used can be different.