## 1 Introduction  
In recent years, the rapid advancement of technology has led to the convergence of solutions such as cloud computing and augmented reality (AR), opening up new frontiers in human experiences. Despite these advances, there are still significant challenges regarding latency, scalability, and real-time processing, especially for applications that require highly interactive and immersive environments. A significant limitation of current systems lies in the centralized nature of cloud computing, which often needs help to meet the rigorous performance requirements for real-time applications, particularly in environments where multiple users must collaborate in a shared virtual space. This has created an urgent need for solutions to bring computational resources closer to the user, minimizing delays and improving overall performance. In this context, edge computing [1; 2] has emerged as a fundamental pillar, providing a decentralized computing infrastructure that enables data processing closer to end users. This proximity reduces latency and improves the responsiveness of critical applications such as collaborative AR. The shift from traditional cloud-based models to edge computing architectures represents a paradigm shift in how data-intensive and latency-sensitive applications are designed and deployed. Collaborative AR, where multiple users can share and interact with virtual objects in real time within a common space, is an excellent example of a technology that benefits significantly from edge computing. This type of AR application requires synchronization among multiple devices, low-latency data transmission, and high computational capacity to maintain smooth interaction and collaboration among users. Failure to meet these requirements can lead to a degraded user experience, making the technology unsuitable for critical sectors such as industrial operations or healthcare, where accuracy and reliability are critical. The interplay between cloud edge computing [3] and collaborative AR can revolutionize these areas by enabling more robust and scalable systems that support real-time collaboration between geographically dispersed users. This paper will explore the connection between these two technologies through case studies, experiments, and theoretical analysis to contribute to the landscape of edge-enabled collaborative systems [4]. Specifically, the focus will be on designing, implementing, and testing different reference architectures for collaborative AR. These architectures will vary in how the computational load is distributed, gradually moving from cloud-centric models to those with more edge-based components. This work is structured as follows: In Section 2, we present a review of the state of the art, exploring current research and technological advances in edge computing and collaborative augmented reality (AR). In Section 3, we outline the specific goals of this research, which include the design, development, and testing of several reference architectures for collaborative AR. Section 4 will provide a detailed definition of the reference architectures with component realizations and sequence diagrams to outline the different modules and their interactions. These architectures will be designed with varying degrees of dependence on cloud resources. In Section 5, the technologies used to implement each architecture will be described. In addition, the several applications we implement will be described and deployed on the different architectures to validate how they work in real-world scenarios. In Section 6, we conduct performance evaluations of the implemented architectures through simulations. These tests, conducted with the Cloud Analyst simulation platform, will evaluate how the defined architectures behave as the number of users scales. Finally, in Section 7, we summarize the main research findings, discuss the broader implications of integrating edge computing with collaborative AR, and suggest directions for future work.