Safeguard Network Slicing in 5G: A Learning Augmented Optimization Approach
Safeguard Network Slicing in 5G: A Learning Augmented Optimization Approach
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DOI：
10.1109/jsac.2020.2999696
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发表时间：
2020-06
影响因子：
16.4
通讯作者：
Xiangle Cheng;Yulei Wu;Geyong Min;Albert Y. Zomaya;X. Fang
中科院分区：
文献类型：
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作者：
Xiangle Cheng;Yulei Wu;Geyong Min;Albert Y. Zomaya;X. Fang
网络切片作为5G的一项关键技术，有望以更高的灵活性、敏捷性和智能性支持所提供的服务和基础设施管理。完成这些任务是具有挑战性的，因为现在的网络越来越异构，动态和大尺寸。这与文献中仅在系统的一个快照上定制即时性能的主流网络切片解决方案相矛盾。相反，本文首先提出了一个两阶段的切片优化模型与时间平均指标，以保障网络切片的动态网络，先验环境知识是不存在的，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，因为未来的系统实现是未知的决策之前。因此，我们提出了一种结合深度学习和李雅普诺夫稳定性理论的学习增强优化方法。这使得系统能够从历史记录和运行时观察中学习安全的切片解决方案。我们证明了所提出的解决方案总是可行的，接近最优的，一个常数的附加因子。最后，我们证明了高达2.6\times $的改进，在模拟时相比，三个国家的最先进的算法。
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.