标题： 面向计算机视觉应用计算卸载的5G-Edge架构 显示英文标题

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

摘要： 在移动设备上处理计算机视觉应用（CVA）具有挑战性，因为电池寿命和计算能力有限。 虽然基于云的CVA远程处理提供了丰富的计算资源，但它会引入延迟问题，这可能阻碍实时应用。 为了解决这个问题，工业界和学术研究已经采用计算卸载到边缘服务器。 此外，5G接入也可以通过比以前的蜂窝代际更低的延迟和更高的带宽来帮助CVA。 随着越来越多的移动运营商和互联网服务提供商依赖5G接入，借助边缘计算支持实时应用的解决方案变得至关重要。 此外，基于开源的多接入边缘计算（MEC）和5G核心平台可以部署用于快速原型设计和测试应用。 本文旨在提供一个端到端的开源MEC和5G核心平台解决方案，以及一个商用5G无线电。 我们首先构思了一个5G-边缘计算环境，以协助接近用户的计算机视觉应用处理。 然后开发并集成了一个情感分析应用到所提出的5G-Edge架构中。 最后，我们对所提出的解决方案进行了性能评估，并将其与基于远程云的方法进行比较，以突出我们提案的优势。 所提出的架构相比基于远程云的卸载，吞吐量性能提高了260%，响应时间减少了71.3%。 显示英文摘要

摘要： 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.