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Characterization and Identification of Cloudified Mobile Network Performance Bottlenecks

机译：阴影移动网络性能瓶颈的特征与识别

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摘要

This study is a first attempt to experimentally explore the range of performance bottlenecks that 5G mobile networks can experience. To this end, we leverage a wide range of measurements obtained with a prototype testbed that captures the key aspects of a cloudified mobile network. We investigate the relevance of the metrics and a number of approaches to accurately and efficiently identify bottlenecks across the different locations of the network and layers of the system architecture. Our findings validate the complexity of this task in the multi-layered architecture and highlight the need for novel monitoring approaches that intelligently fuse metrics across network layers and functions. In particular, we find that distributed analytics performs reasonably well both in terms of bottleneck identification accuracy and incurred computational and communication overhead.
机译：本研究首次尝试通过实验探索5G移动网络可以体验的性能瓶颈范围。为此，我们利用了使用原型测试的广泛测量，该原型测试平台捕获了混阴影移动网络的关键方面。我们调查指标和许多方法的相关性，以准确和有效地识别跨网络和系统架构层的不同位置的瓶颈。我们的调查结果验证了多层体系结构中这项任务的复杂性，并突出了跨网络层和功能智能熔断指标的新颖监视方法的需要。特别是，我们发现分布式分析在瓶颈识别准确性和沟通和通信开销方面都能合理地表现出相当好。

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