How to use MEC and ML to Improve Resources Allocation in SDN Networks ?

M. Abderrahim, Asma BEN LETAIFA, Amel Haji, S. Tabbane
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引用次数: 3

Abstract

One of the important objectives of service providers in the 5G network is to improve the use of network resources to provide multiple types of services with a good quality of service. In this context, the MEC (Mobile Edge Computing) presents a new opportunity that allows hosting applications close to end users with a reduction of latency and performance improvement. In this article we will propose a new architecture that improves the fast and efficient delivery of new applications, based on the concept of MEC and the important role of machine learning algorithms. The proposed architecture will help network operators to better exploit network resources and improve their services. Indeed, it ensures the collection of radio information, the prediction of necessary needs and the dynamic and efficient sharing of network resources.
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如何利用MEC和ML改善SDN网络的资源分配?
运营商在5G网络中的重要目标之一是提高网络资源的利用率,以良好的服务质量提供多种类型的业务。在这种情况下,MEC(移动边缘计算)提供了一个新的机会,允许托管应用程序靠近最终用户,减少延迟并提高性能。在本文中,我们将基于MEC的概念和机器学习算法的重要作用,提出一种新的架构,以提高新应用程序的快速高效交付。建议的架构将有助于网络运营商更好地利用网络资源和改善其服务。实际上,它保证了无线电信息的收集,必要需求的预测和网络资源的动态和有效共享。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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