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A Review on AI-Enabled Congestion Control Schemes for Content Centric Networks

Authors
Masood, AroojDao, Nhu-NgocKim, HyosuSon, YongseokLee, Hyung TaePaek, JeongyeupCho, Sungrae
Issue Date
2023
Publisher
IEEE Computer Society
Keywords
congestion control; Content centric networks; in-network caching
Citation
International Conference on ICT Convergence, v.2023 14th, pp 659 - 662
Pages
4
Journal Title
International Conference on ICT Convergence
Volume
2023 14th
Start Page
659
End Page
662
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/72870
DOI
10.1109/ICTC58733.2023.10393556
ISSN
2162-1233
Abstract
Content centric networks (CCN) offer more advantages over conventional TCP/IP networks in areas like content distribution. However, congestion control functionalities in CCN present challenges such as detecting congestion, over-reducing windows for non-congested paths, and addressing fairness issues. Most existing studies employed congestion control mechanisms similar to those in TCP. In addition, the existing mechanisms were based on conventional optimization rules to adjust the rate at which Interest packets are sent to request data from downstream nodes. However, such existing mechanisms do not consider the changes in network status and caching strategy due to multi-path and multi-source transmission. Moreover, they are based on assumptions about link bandwidth. In this paper, we study the problem of congestion control in CCN and discuss its challenges. In addition, we review the existing congestion control schemes in CCN based on machine learning. Finally, we highlight the open research issues to spur further investigations. © 2023 IEEE.
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소프트웨어대학 (소프트웨어학부)
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