Review of Congestion Control Methods in Named Data Network

Authors

  • Bohan Zhang

DOI:

https://doi.org/10.62051/ijcsit.v3n1.09

Keywords:

Named data networking, Congestion control, Receiver-driven

Abstract

In recent years, with the development of new networking technologies, the demand for networks has shifted from simple resource sharing to large-scale content distribution. This change has led to the gradual inadequacy of the traditional TCP/IP architecture in meeting the current needs of production and daily life. In this context, Named Data Networking (NDN) has emerged. NDN networks, with their content-centric nature and features such as in-network caching, multi-source forwarding, and routing control, have attracted significant attention from researchers both domestically and internationally. However, the novel architecture of the network also presents new challenges for congestion control research in NDN. This paper conducts thorough research on the related work of congestion control in NDN, provides a detailed overview of existing research efforts in NDN congestion control, and describes potential future research directions in NDN congestion control. The aim is to provide valuable references for future research on congestion control in NDN.

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References

Tariq A, Rehman R A, Kim B S. Forwarding strategies in NDN-based wireless networks: A survey [J]. IEEE Communications Surveys & Tutorials, 2019, 22(1): 68-95.

Balador A, Cinque E, Pratesi M, et al. Survey on decentralized congestion control methods for vehicular communication [J]. Vehicular Communications, 2022, 33: 100394.

Iqbal S , Hoque M, et al. A source-driven probabilistic forwarding and caching strategy in NDN and SDN-based NDN [J]. International Journal of Communication Systems, 2022, 35(6): e5093.

Chaudhary P, Hubballi N, et al. eNCache: Improving content delivery with cooperative caching in Named Data Networking [J]. Computer Networks, 2023, 237: 110104.

Donta P K, Srirama S N, Amgoth T, et al. iCoCoA: Intelligent congestion control algorithm for CoAP using deep reinforcement learning [J]. Journal of Ambient Intelligence and Humanized Computing, 2023, 14(3): 2951-2966.

Mazziane Y B, Alouf S, Neglia G, et al. TTL model for an LRU-based similarity caching policy [J]. Computer Networks, 2024, 241: 110206.

Carofiglio G, Gallo M, et al. ICP: Design and evaluation of an interest control protocol for content-centric networking [C]. 2012 Proceedings IEEE INFOCOM Workshops. IEEE, 2012: 304-309.

Carofiglio G, Gallo M, Muscariello L, et al. Multipath congestion control in content-centric networks [C]//2013 IEEE conference on computer communications workshops (INFOCOM WKSHPS). IEEE, 2013: 363-368.

Y. Ren, J. Li, S. Shi, L. Li, and G. Wang, “An explicit congestion control algorithm for named data networking,” in 2016 IEEE conference on computer communications workshops (INFOCOM WKSHPS). IEEE, 2016, pp. 294–299.

Xing S, Yin B, Yao J, et al. A VCP-based congestion control algorithm in named data networking [C]. 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 2018: 463-468.

Ye Y, Lee B, Qiao Y. Hop-by-hop congestion measurement and practical active queue management in NDN [C]//GLOBECOM 2020-2020 IEEE Global Communications Conference. IEEE, 2020: 1-6.

Kato T, Bandai M, Yamamoto M. A congestion control method for named data networking with hop-by-hop window-based approach [J]. IEICE Transactions on Communications, 2019, 102(1): 97-110.

Wang Y, Rozhnova N, Narayanan A, et al. An improved hop-by-hop interest shaper for congestion control in named data networking [J]. ACM SIGCOMM Computer Communication Review, 2013, 43(4): 55-60.

Yi C, Afanasyev A, Moiseenko I, et al. A case for stateful forwarding plane [J]. Computer Communications, 2013, 36(7): 779-791.

Rozhnova N, Fdida S. An effective hop-by-hop interest shaping mechanism for ccn communications [C]. IEEE INFOCOM Workshops. IEEE, 2012: 322-327.

Li Z, Shen X, Xun H, et al. CoopCon: Cooperative hybrid congestion control scheme for named data networking [J]. IEEE Transactions on Network and Service Management, 2023.

Agarwal A, Tahiliani M P. BCON: Back pressure based congestion avoidance model for Named Data Networks [C]//2016 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS). IEEE, 2016: 1-5.

Carofiglio G, Gallo M, et al. Joint hop-by-hop and receiver-driven interest control protocol for content-centric networks [J]. ACM SIGCOMM Computer Communication Review, 2012, 42(4): 491-496.

Zhang F, Zhang Y, Reznik A, et al. A transport protocol for content-centric networking with explicit congestion control [C]. International conference on computer communication and networks (ICCCN). IEEE, 2014: 1-8.

Ye Y, Lee B, Flynn R, et al. B-icp: Backpressure interest control protocol for multipath communication in ndn [C]. IEEE Global Communications Conference. IEEE, 2017: 1-6.

Zhong S, Liu Y, Li J, et al. A rate-based multipath-aware congestion control mechanism in named data networking [C]//2017 IEEE international symposium on parallel and distributed processing with applications and 2017 IEEE international conference on ubiquitous computing and communications (ISPA/IUCC). IEEE, 2017: 174-181.

Dong M, Li Q, Zarchy D, et al. PCC: Re-architecting congestion control for consistent high performance [C]//12th Symposium on Networked Systems Design and Implementation. 2015: 395-408.

Dong M, Meng T, Zarchy D, et al. PCC vivace: Online-learning congestion control [C]//15th Symposium on Networked Systems Design and Implementation. 2018: 343-356.

Li W, Zhou F, Chowdhury K R, et al. QTCP: Adaptive congestion control with reinforcement learning [J]. IEEE Transactions on Network Science and Engineering, 2018, 6(3): 445-458.

Narayan A, Cangialosi F, Raghavan D, et al. Restructuring endpoint congestion control [C]//Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication. 2018: 30-43.

Liu T, Zhang M, Zhu J, et al. ACCP: adaptive congestion control protocol in named data networking based on deep learning [J]. Neural Computing and Applications, 2019, 31: 4675-4683.

Yang J, Chen Y, Xue K, et al. IEACC: an intelligent edge-aided congestion control scheme for named data networking with deep reinforcement learning [J]. IEEE Transactions on Network and Service Management, 2022, 19(4): 4932-4947.

Lan D, Tan X, Lv J, et al. A deep reinforcement learning based congestion control mechanism for NDN [C]//ICC 2019-2019 IEEE International Conference on Communications (ICC). IEEE, 2019: 1-7.

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Published

15-06-2024

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Articles

How to Cite

Zhang, B. (2024). Review of Congestion Control Methods in Named Data Network. International Journal of Computer Science and Information Technology, 3(1), 55-62. https://doi.org/10.62051/ijcsit.v3n1.09