Edge computing is projected to become the dominant form of cloud computing in the future because of the significant advantages it brings to both users (less latency, higher throughput) and telecom operators (less Internet traffic, more local management). However, to fully unlock its potential at scale, system designers and automated optimization systems alike will have to monitor closely the dynamics of both processing and communication facilities. Especially the latter is often neglected in current systems since network performance in cloud computing plays only a minor role. In this paper, we propose the architecture of MECPerf, which is a solution to collect network measurements in a live edge computing domain, to be collected for offline provisioning analysis and simulations, or to be provided in real-time for on-line system optimization. MECPerf has been validated in a realistic testbed funded by the European Commission (Fed4Fire+), and we describe here a summary of the results, which are fully available as open data and through a Python library to expedite their utilization. This is demonstrated via a use case involving the optimization of a system parameter for migrating clients in a federated edge computing system adopting the GSMA platform operator concept.

边缘计算预计将在未来成为云计算的主要形式，因为它为用户（更少的延迟，更高的吞吐量）和电信运营商（更少的Internet流量，更多的本地管理）带来了显着的优势。但是，要完全释放其潜力，系统设计人员和自动化优化系统都必须密切监视处理和通讯设施的动态。特别是后者在当前系统中经常被忽略，因为云计算中的网络性能仅扮演次要的角色。在本文中，我们提出了MECPerf的体系结构，该体系结构是一种解决方案，用于在实时边缘计算域中收集网络测量结果，可以收集以进行脱机配置分析和仿真，也可以实时提供以进行在线系统优化。 。MECPerf已在由欧盟委员会（Fed4Fire +）资助的真实测试床上进行了验证，我们在这里描述结果的摘要，这些摘要可以作为开放数据使用，也可以通过Python库完全使用，以加快其使用速度。这通过一个用例进行了演示，该用例涉及在采用GSMA平台操作员概念的联合边缘计算系统中优化用于迁移客户端的系统参数。