Safeguard Network Slicing in 5G: A Learning Augmented Optimization Approach
Cheng, X; Wu, Y; Min, G; et al.Zomaya, A; Fang, X
Date: 3 June 2020
Article
Journal
IEEE Journal on Selected Areas in Communications
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Publisher DOI
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 ...
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× improvement in the simulation when compared with three
state-of-the-art algorithms.
Computer Science
Faculty of Environment, Science and Economy
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