基于机器学习方法的多域蜂窝网络业务迁移

2020 7th International Conference on Internet of Things: Systems, Management and Security (IOTSMS) Pub Date : 2020-12-14 DOI:10.1109/IOTSMS52051.2020.9340223

Marco Pomalo, V. T. Le, Nabil El Ioini, C. Pahl, H. Barzegar

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引用次数: 8

摘要

随着长期演进(4G) (LTE)和即将到来的第5代移动通信(5G)等高性能移动通信网络的部署，移动用户数量急剧增加。在蜂窝网络中，无缝连接是提供更好的服务质量(QoS)和体验质量(QoE)的重要因素。在这方面，利用移动边缘计算(MEC)技术可以使所需的移动服务更接近最终用户，从而减少延迟，提高服务质量。然而，为了满足预期的服务质量并保证服务连续性，需要优化MEC节点之间的切换。本文通过提出一种方法和预测算法来解决这一问题，以提高MEC配置中的SC。

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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Service Migration in Multi-domain Cellular Networks based on Machine Learning Approaches

The number of mobile subscribers has increased drastically with the deployment of high-performance mobile cellular networks such as Long Term Evolution (4G) (LTE), and the upcoming 5th Generation Mobile Cellular Network (5G). Seamless connectivity is an important factor to provide better Quality-of-Service (QoS) as well as Quality-of-Experience (QoE) in cellular networks. In this regard, the utilization of Mobile Edge Computing (MEC) technology allows to bring required mobile services close to the end users to reduce latency, and increase service quality. However, switching between MECs nodes needs to be optimized in order to satisfy the expected quality of service as well as to guarantee service continuity SC. This paper addresses this problem by proposing a methodology and a prediction algorithm to boost SC in a MEC configuration.

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来源期刊

2020 7th International Conference on Internet of Things: Systems, Management and Security (IOTSMS)

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