发表机构
University of Turin（都灵大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过MvDet模型和FastFL框架实验验证计算连续体在分布式边缘推理中的优势，相比集中式方案获得高达1.92倍推理加速。
AI 中文摘要
计算正在快速发展以满足对复杂服务日益增长的需求，云计算为灵活的按需供应奠定了坚实基础。然而，随着应用规模的扩大，云计算所使用的集中式客户端-服务器方法日益限制应用的可扩展性。为实现超可扩展性，云/边缘/雾计算融合为计算连续体，完全去中心化基础设施以涵盖普遍、泛在的资源。计算连续体使得设计受益于这一复杂环境的应用程序成为一个具有挑战性的研究问题。我们通过使用FastFL C/C++高性能边缘推理框架实现的真实世界多视角检测模型（MvDet）来检验计算连续体所提供的机会。我们讨论了多种实验场景下的计算性能，涵盖不同的边缘计算能力和网络带宽。与使用相同设备的集中式解决方案相比，我们在推理时间上获得了高达1.92倍的加速。
英文摘要
Computing is evolving rapidly to cater to the increasing demand for sophisticated services, and Cloud computing lays a solid foundation for flexible on-demand provisioning. However, as the size of applications grows, the centralised client-server approach used by Cloud computing increasingly limits the applications' scalability. To achieve ultra-scalability, cloud/edge/fog computing converges into the compute continuum, completely decentralising the infrastructure to encompass universal, pervasive resources. The compute continuum makes devising applications benefitting from this complex environment a challenging research problem. We put the opportunities the compute continuum offers to the test through a real-world multi-view detection model (MvDet) implemented with the FastFL C/C++ high-performance edge inference framework. Computational performance is discussed considering many experimental scenarios, encompassing different edge computational capabilities and network bandwidths. We obtain up to 1.92x speedup in inference time over a centralised solution using the same devices.
Journal refProceedings of the {IEEE/ACM} 16th International Conference on Utility and Cloud Computing, {UCC} 2023, Taormina (Messina), Italy, December 4-7, 2023
