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Deep Deterministic Policy Gradient-Based Load Balancing Method in SDN Environments
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Publication Year
2024-01-01
Journal
2024 International Conference on Smart Applications, Communications and Networking, SmartNets 2024
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
2024 International Conference on Smart Applications, Communications and Networking, SmartNets 2024
Keyword
DDPGDRLLoad BalancingQoSSDN
Mesh Keyword
Deep deterministic policy gradientDeep reinforcement learningDeterministicsIn networksLoad-BalancingNetworks managementPolicy gradientQuality-of-serviceReinforcement learningsSoftware-defined networkings
All Science Classification Codes (ASJC)
Artificial IntelligenceComputer Networks and CommunicationsSignal Processing
Abstract
Software-Defined Networking (SDN) is a transfor-mative technology that separates the control and data planes, facilitating network control and management through centralized or distributed SDN controllers. Recent advancements in machine learning, specifically Deep Reinforcement Learning (DRL), drive research towards integrating these techniques into SDN for intelligent network management. Effective load balancing in networks is a critical aspect of network management, aiming to enhance Quality of Service (QoS). While previous research has explored load balancing for SDN using DRL, they persist in small-scale topologies, and challenges remain in adapting to dynamic changes in network traffic on links in large-scale topolo-gies. This paper proposes a novel approach to load balancing in large-scale SDN networks utilizing Deep Deterministic Policy Gradient (DDPG). Additionally, the paper proposes a method to extract optimal parameters, enhancing the overall effectiveness of network management.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/37148
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85198906202&origin=inward
DOI
https://doi.org/10.1109/smartnets61466.2024.10577651
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10577635
Type
Conference
Funding
\\\This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2023-2018-0-01431) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation)\\\
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Roh, Byeong-hee Image
Roh, Byeong-hee노병희
Department of Software and Computer Engineering
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