A 5G-Edge Architecture for Computational Offloading of Computer Vision Applications

TL;DR

This paper presents a 5G-edge computing architecture for real-time computer vision applications, demonstrating significant performance improvements over cloud-based processing through open-source MEC and 5G core integration.

Contribution

It introduces an end-to-end open-source 5G-edge architecture for CVA offloading, including a prototype with performance evaluation against cloud-based solutions.

Findings

260% throughput increase

71.3% response time reduction

Effective real-time CVA processing at the edge

Abstract

Processing computer vision applications (CVA) on mobile devices is challenging due to limited battery life and computing power. While cloud-based remote processing of CVA offers abundant computational resources, it introduces latency issues that can hinder real-time applications. To overcome this problem, computational offloading to edge servers has been adopted by industry and academic research. Furthermore, 5G access can also benefit CVA with lower latency and higher bandwidth than previous cellular generations. As the number of Mobile Operators and Internet Service providers relying on 5G access is growing, it is of paramount importance to elaborate a solution for supporting real time applications with the assistance of the edge computing. Besides that, open-source based platforms for Multi-access Edge Computing (MEC) and 5G core can be deployed to rapid prototyping and testing applications. This paper aims at providing an end-to-end solution of open-source MEC and 5G Core platforms along with a commercial 5G Radio. We first conceived a 5G-edge computing environment to assist near to user processing of computer vision applications. Then a sentiment analysis application is developed and integrated to the proposed 5G-Edge architecture. Finally, we conducted a performance evaluation of the proposed solution and compare it against a remote cloud-based approach in order to highlight the benefits of our proposal. The proposed architecture achieved a 260\% throughput performance increase and reduced response time by 71.3\% compared to the remote-cloud-based offloading.