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Safeguard Network Slicing in 5G: A Learning Augmented Optimization Approach
IEEE Journal on Selected Areas in Communications ( IF 16.4 ) Pub Date : 2020-07-01 , DOI: 10.1109/jsac.2020.2999696
Xiangle Cheng , Yulei Wu , Geyong Min , Albert Y. Zomaya , Xuming Fang

Network slicing, as a key 5G enabling technology, is promising to support with more flexibility, agility, and intelligence towards the provisioned services and infrastructure management. Fulfilling these tasks is challenging, as nowadays networks are increasingly heterogeneous, dynamic and large-dimensioned. This contradicts the dominant network slicing solutions that only customize immediate performance over one snapshot of the system in the literature. Instead, this paper first presents a two-stage slicing optimization model with time-averaged metrics to safeguard the network slicing in the dynamical networks, where prior environmental knowledge is absent but can be partially observed at runtime. Directly solving an off-line solution to this problem is intractable since the future system realizations are unknown before decisions. Therefore, we propose a learning augmented optimization approach with deep learning and Lyapunov stability theories. This enables the system to learn a safe slicing solution from both historical records and run-time observations. We prove that the proposed solution is always feasible and nearly optimal, up to a constant additive factor. Finally, we demonstrate up to $2.6\times $ improvement in the simulation when compared with three state-of-the-art algorithms.

中文翻译:

保护 5G 中的网络切片:一种学习增强优化方法

网络切片作为一项关键的 5G 使能技术,有望以更高的灵活性、敏捷性和智能性支持配置服务和基础设施管理。完成这些任务具有挑战性,因为如今网络变得越来越异构、动态和大维度。这与主要的网络切片解决方案相矛盾,后者仅在文献中针对系统的一个快照定制即时性能。相反,本文首先提出了一个具有时间平均度量的两阶段切片优化模型,以保护动态网络中的网络切片,其中不存在先验环境知识但可以在运行时部分观察。直接解决这个问题的离线解决方案是棘手的,因为在做出决定之前,未来的系统实现是未知的。所以,我们提出了一种具有深度学习和李雅普诺夫稳定性理论的学习增强优化方法。这使系统能够从历史记录和运行时观察中学习安全切片解决方案。我们证明了所提出的解决方案始终可行且接近最优,直到一个恒定的附加因子。最后,与三种最先进的算法相比,我们在模拟中展示了高达 $2.6\times $ 的改进。
更新日期:2020-07-01
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