基于容器的边缘计算系统的工程和实验基准测试
Engineering and Experimentally Benchmarking a Container-based Edge Computing System
摘要：
虽然边缘计算被设想为出色地服务于延迟敏感的应用程序，但基于实现的基准测试其性能的研究却很少。为了解决这一差距，我们设计了一个模块化边缘云计算系统架构，该架构基于容器化技术的最新进展，包括用于数据流的Kafka，作为应用程序平台的Docker和作为实时数据库系统的Firebase Cloud。我们通过比较三种场景：仅云，仅边缘和组合边缘云，在可扩展性，资源利用率和延迟方面对系统的性能进行基准测试。测量结果表明，仅边缘解决方案只有在部署仅位于一个边缘的数据时才优于其他场景，即，而无需边缘计算宽数据同步。在需要通过云进行数据同步的应用程序的情况下，边缘云的扩展比纯云好10倍左右，直到系统中有一定数量的并发用户，并且超过这一点，纯云的扩展效果更好。在资源利用率方面，我们观察到，虽然平均利用率随着用户请求的数量线性增加，但随着数据量的增加，内存和网络I/O的最大值会大幅增加。
英文摘要：
While edge computing is envisioned to superbly serve latency sensitive applications, the implementation-based studies benchmarking its performance are few and far between. To address this gap, we engineer a modular edge cloud computing system architecture that is built on latest advances in containerization techniques, including Kafka, for data streaming, Docker, as application platform, and Firebase Cloud, as realtime database system. We benchmark the performance of the system in terms of scalability, resource utilization and latency by comparing three scenarios: cloud-only, edge-only and combined edge-cloud. The measurements show that edge-only solution outperforms other scenarios only when deployed with data located at one edge only, i.e., without edge computing wide data synchronization. In case of applications requiring data synchronization through the cloud, edge-cloud scales around a factor 10 times better than cloud-only, until certain number of concurrent users in the system, and above this point, cloud-only scales better. In terms of resource utilization, we observe that whereas the mean utilization increases linearly with the number of user requests, the maximum values for the memory and the network I/O heavily increase when with an increasing amount of data.
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。
