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A deep reinforcement learning-based resource management scheme for SDN-MEC-supported XR applications

Trinh, Bao ORCID: 0000-0003-1014-2179 and Muntean, Gabriel-Miro ORCID: 0000-0002-9332-4770 (2022) A deep reinforcement learning-based resource management scheme for SDN-MEC-supported XR applications. In: IEEE Consumer Communications & Networking Conference 2022, 8 - 11 Jan 2022, Virtual. ISBN 978-1-6654-3161-3

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Abstract

The Multi-Access Edge Computing (MEC) paradigm provides a promising solution for efficient computing services at edge nodes, such as base stations (BS), access points (AP), etc. By offloading highly intensive computational tasks to MEC servers, critical benefits in terms of reducing energy consumption at mobile devices and lowering processing latency can be achieved to support high Quality of Service (QoS) to many applications. Among the services which would benefit from MEC deployments are eXtended Reality (XR) applications which are receiving increasing attention from both academia and industry. XR applications have high resource requirements, mostly in terms of network bandwidth, computation and storage. Often these resources are not available in classic network architectures and especially not when XR applications are run by mobile devices. This paper leverages the concepts of Software Defined Networking (SDN) and Network Function Virtualization (NFV) to propose an innovative resource management scheme considering heterogeneous QoS requirements at the MEC server level. The resource assignment is formulated by employing a Deep Reinforcement Learning (DRL) technique to support high quality of XR services. The simulation results show how our proposed solution outperforms other state-of-the-art resource management-based schemes.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:SDN; NFV; Edge computing; QoS; extended Reality
Subjects:Computer Science > Computer networks
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: IEEE Consumer Communications & Networking Conference 2022, Proceedings. . IEEE. ISBN 978-1-6654-3161-3
Publisher:IEEE
Official URL:https://ccnc2022.ieee-ccnc.org/shield-page?destination=node/222&back=ccnc-2022-conference-proceedings&shield_page=1
Copyright Information:© 2022 IEEE
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland Grant Number SFI/12/RC/2289 P2, European Regional Development Fund
ID Code:26600
Deposited On:13 Jan 2022 10:58 by Bao Trinh . Last Modified 13 Jan 2022 10:58

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