1. Safeguard Network Slicing in 5G: A Learning Augmented Optimization Approach
- Author
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Albert Y. Zomaya, Geyong Min, Xuming Fang, Xiangle Cheng, and Yulei Wu
- Subjects
Computer Networks and Communications ,business.industry ,Computer science ,Deep learning ,Distributed computing ,Knowledge engineering ,020206 networking & telecommunications ,Provisioning ,Lyapunov optimization ,02 engineering and technology ,Slicing ,Safeguard ,0202 electrical engineering, electronic engineering, information engineering ,Artificial intelligence ,Electrical and Electronic Engineering ,business ,5G - Abstract
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.
- Published
- 2020