Dependability modeling and analysis of augmented reality systems with edge offloading

Abstract Augmented reality (AR) applications are revolutionizing healthcare, engineering, and education sectors, enabling users to integrate virtual elements into real-world environments seamlessly. Despite advancements in mobile computing, AR systems face significant challenges due to the computational limitations of mobile devices, particularly in scenarios demanding low latency and high reliability. Offloading computationally intensive tasks to edge servers presents a promising solution, but also introduces complexities in ensuring dependability. This paper proposes a process for evaluating the availability and reliability of AR systems with edge offloading. We analyze various redundancy strategies and their impact on system dependability by leveraging hierarchical modeling techniques, including reliability block diagrams and continuous-time Markov chains. Our findings reveal that adopting hot-standby redundancy and optimizing critical components, such as network infrastructure and mobile device batteries, significantly enhances system availability. The study concludes with a detailed case analysis, demonstrating how the proposed models can guide infrastructure planning for AR applications. This work provides actionable insights for developers and researchers seeking to design robust AR systems that can meet the stringent dependability requirements of real-world deployments.