选择有效的流处理设置
Choosing an effective setup for stream processing
摘要：
该项目旨在研究在欧洲制造业物联网（IoT）背景下使用边缘计算进行流数据处理的可行性和成本效益。考虑了两种情况：使用边缘计算来减少延迟和使用流行的公共云提供商。这两种场景都表现出了高吞吐量，边缘计算场景的性能略优于公共云场景。还测量了对资源利用率的影响，边缘节点的资源使用率略低于云节点。实验得出的结论是，在边缘运行系统更具成本效益，但只能使用任何基础设施即服务（IaaS）提供商作为基础设施提供商。IaaS提供商在提供边缘解决方案和确定区域数据中心可用作低延迟应用程序存在点的地理区域方面至关重要。保留字：边缘计算、流数据处理、物联网（IoT）、制造业、欧洲、延迟、吞吐量、资源利用率、成本效益、基础设施即服务（IaaS）、区域数据中心、低延迟应用、云计算、可行性研究。
英文摘要：
This project aims to study the feasibility and cost-effectiveness of using edge computing for stream data processing in the context of Internet of Things (IoT) in manufacturing in Europe. Two scenarios were considered: using edge computing to reduce latency and using a popular public cloud provider. Both scenarios demonstrated high throughput, with the edge computing scenario slightly outperforming the public cloud scenario. The impact on resource utilization was also measured, with the edge node showing slightly lower resource usage than the cloud node. The experiment concluded that running the system at the edge is more cost-efficient, but only using any Infrastructure as a Service (IaaS) provider acting as the infrastructure provider. IaaS providers will be crucial in offering edge solutions and identifying geographical areas where regional data centers could be used as points of presence for low-latency applications. Keywords: edge computing, stream data processing, Internet of Things (IoT), manufacturing, Europe, latency, throughput, resource utilization, cost-efficiency, infrastructure as a service (IaaS), regional data centers, low-latency applications, cloud computing, feasibility study.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
