定义 5G 系统中基于测量的 MEC 节点模型的框架

Riccardo Fedrizzi;Cristina Emilia Costa;Fabrizio Granelli
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引用次数: 0

摘要

多接入边缘计算(MEC)是一种新兴的解决方案,可解决现代和未来移动网络中与可靠性、可用性、上下文感知和低延迟相关的问题。尽管人们对 MEC 解决方案的开发兴趣浓厚并付出了巨大努力,但文献中只有少数作品对 MEC 节点的可用资源和相关性能之间的现有权衡进行了建模。这封信旨在填补这一空白,提出了一种基于测量的方法,为任何类型的 MEC 节点间接推导出一个有效的模型,能够捕捉不同性能参数之间的现有权衡。从根本上说,我们的想法是描述所提供的计算和通信负载与可实现性能之间的输入输出关系。网络模拟器用于生成数据以定义模型。结果通过分析 CPU 和网络限制下 MEC 节点样本的不同权衡和可实现性能,证明了这一想法的概念。
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A Framework to Define a Measurement-Based Model of MEC Nodes in the 5G System
Multi-access edge computing (MEC) represents an emerging solution to address the issues related to reliability, availability, context awareness and low latency in modern and future mobile networks. Despite the great interest and effort in developing MEC solutions, only a few works are available in the literature to model the existing trade-offs between available resources and the related performance of MEC nodes. This letter aims to fill this gap by proposing a measurement-based approach to indirectly derive an effective model for any type of MEC node, capable of capturing the existing trade-offs among different performance parameters. Basically, the idea is to characterize the input-output relationship between offered computational and communication load and achievable performance. A network emulator is used to generate the data to define the model. The results provide proof-of-concept of idea by analyzing the different trade-offs and achievable performance of a sample MEC node under CPU and networking limitations.
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Table of Contents IEEE Networking Letters Author Guidelines IEEE COMMUNICATIONS SOCIETY IEEE Communications Society Optimal Classifier for an ML-Assisted Resource Allocation in Wireless Communications
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