On the Deployability of Augmented Reality Using Embedded Edge Devices

TL;DR

This paper evaluates the feasibility of deploying augmented reality applications on embedded edge devices versus server-grade hardware, highlighting the trade-offs in responsiveness and user capacity.

Contribution

It provides an empirical assessment and a simple theoretical model to compare embedded edge devices with server-grade solutions for AR deployment.

Findings

Server-grade hardware supports higher responsiveness but fewer users.

Embedded edge devices enable distributed deployment supporting more users.

Centralized server solutions are limited in scalability for high responsiveness applications.

Abstract

Edge Computing exploits computational capabilities deployed at the very edge of the network to support applications with low latency requirements. Such capabilities can reside in small embedded devices that integrate dedicated hardware -- e.g., a GPU -- in a low cost package. But these devices have limited computing capabilities compared to standard server grade equipment. When deploying an Edge Computing based application, understanding whether the available hardware can meet target requirements is key in meeting the expected performance. In this paper, we study the feasibility of deploying Augmented Reality applications using Embedded Edge Devices (EEDs). We compare such deployment approach to one exploiting a standard dedicated server grade machine. Starting from an empirical evaluation of the capabilities of these devices, we propose a simple theoretical model to compare the performance of the two approaches. We then validate such model with NS-3 simulations and study their feasibility. Our results show that there is no one-fits-all solution. If we need to deploy high responsiveness applications, we need a centralized server grade architecture and we can in any case only support very few users. The centralized architecture fails to serve a larger number of users, even when low to mid responsiveness is required. In this case, we need to resort instead to a distributed deployment based on EEDs.