Secure and energy-efficient resource allocation in network slicing

2022-9-2
Gülmez, Umut Can
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The one-size-fits-all idea of the previous telecommunication generations is no longer suitable for current applications. The current network systems need to satisfy the Quality of Service requirements of the different types of use cases such as enhanced mobile broadband, ultra-reliable and low latency communications and massive machine type communications in the same physical infrastructure. 5G telecommunication networks aim to provide a solution to this problem through the network slicing concept. Virtual Network Functions (VNF) play an essential role in the network slicing concept and embedding these functions into the network is an important task to achieve. As state-of-the-art research focuses on allocating these functions in-network taking only energy efficiency into consideration, this research proposes a solution that considers the security aspects too. We propose a VNF placement strategy using an integer linear programming (ILP) model for 5G network slicing under strict security requirements, which optimizes energy consumption by the core network nodes. As an improvement to this approach, we also propose using Deep Reinforcement Learning (DRL) methods to provide a dynamic, energy-efficient, resilient, and secure resource allocation framework for network slicing. Hence, in this research, we compared varv ious state-of-the-art DRL methods to find a suitable algorithm for energy-efficient resource allocation under stringent security constraints. Simulation results demonstrate that the proposed DRL-based models achieve significant energy optimization compared to the ILP-based optimization model performing VNF placement under the same QoS and security constraints. The results of the study show that DRL-based methods provide faster allocation than ILP-based methods. Also, the results show that DRL-based methods provide faster allocation than ILP-based methods.
5G and Beyond Mobile Networks, Network Slicing, Deep Reinforcement Learning, Integer Linear Programming, Network Security, Energy Efficiency
https://hdl.handle.net/11511/99546
Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats
U. C. Gülmez, “Secure and energy-efficient resource allocation in network slicing,” M.S. - Master of Science, Middle East Technical University, 2022.
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