## 2 State of the Art
Regarding Internet of Things (IoT) applications requiring high computing power and real-time processing, in [6], the authors propose to face these challenges with low-cost Small Board Computers (SBCs) by leveraging container-based technologies and fog computing to enhance device collaboration, scalability and load balancing. They compare the container orchestration platforms Docker Swarm and Kubernetes on SBC clusters, finding that Docker Swarm provides superior performance across various topologies and cluster configurations. In [2], the authors face the problem of container and microservice management platforms like Kubernetes, which are crucial for Cloud computing but struggle with heterogeneous computing environments, where nodes have varied characteristics. They propose to replace Kubernetes' native scheduler with a customizable one that better accommodates the diverse needs of a Continuum Computing environment. Testing a batch-based scheduling approach on virtual machines demonstrates improved performance over Kubernetes' pod-by-pod method, highlighting the limitations of default scheduling in supporting complex deployment requirements. In [14], the author exposes in a seminar a comparison between hypervisor-based and container-based virtualization, highlighting the rise in popularity of container-based virtualization due to Docker, a tool that simplifies container management. While hypervisor-basedvirtualization has been a dominant method for decades, container-based virtualization offers distinct advantages, leading to its increased use. The paper details the benefits and tradeoffs of both technologies, with a particular focus on container-based virtualization and Docker. It also addresses the security risks associated with containerization and provides a summary of related work in the field. Different authors have tackled the integration of virtualization/containerization with Edge Computing. In [15], the authors investigate different works on Cloud and Edge Computing paradigms and their virtualization at the Edge. They also provide a taxonomy based on the state of the art for virtualized resources. In [16], the author analyzes the application of the concept of device virtualization in IoT/Edge Computing and carries out performance studies using single-board computers (SBCs) to efficiently deploy different low-power virtualized devices (i.e., families of Raspberry Pi and Odroid) in terms of performance when handling container-virtualized instances. Also, in [17], the authors focus their efforts in evaluating a methodology based on the study of containerization-based Edge-Cloud computing infrastructures' performance. They saw that containerization at the Edge does not introduce noticeable performance degradation in terms of communication, computing and intelligence capabilities, making it a promising technology for the Edge-Cloud computing paradigm, although they claim that there is still room for improvement, especially in time-critical industrial applications. According to them, this evaluation methodology can be used as a reference for practitioners in Edge-Cloud computing to obtain a client-perspective overview of system performance. The massive amount of data generation today challenges current network architectures, which struggle with bandwidth, latency and privacy issues. From the Edge Computing perspective, these problems are addressed by enabling local data processing, while Network Function Virtualization (NFV) simplifies service creation by virtualizing traditional network functions. In [5], the authors compare containers and virtual machines through the implementation of a unified management and orchestration platform (MANO) for Virtualized Everything Functions (VxFs) using LXD for containers and KVM for virtual machines on a Raspberry Pi 4. The results indicate that containerization is not always suitable alone and is best used as a complementary approach to full virtualization.