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.

翻译：边缘计算预计将成为未来云计算的主导形态，因为它为用户（更低延迟、更高吞吐量）和电信运营商（减少互联网流量、增强本地管理）均带来显著优势。然而，要在大规模场景下充分释放其潜力，系统设计者和自动化优化系统都必须密切监控处理设施和通信设施的动态变化。后者在当前系统中往往被忽视，因为网络性能在云计算中仅起次要作用。本文提出了MECPerf架构——一种在实时边缘计算域中采集网络测量数据的解决方案，这些数据既可用于离线规划分析和仿真，也可实时提供给在线系统优化。MECPerf已在欧盟委员会资助的Fed4Fire+真实测试平台上完成验证，本文简要总结了相关结果。所有结果均以开放数据形式公开，并通过Python库加速其应用。我们通过一个用例展示了其有效性：在采用GSMA平台运营商概念的联邦边缘计算系统中，优化客户端迁移的系统参数。