Abstract
Named data networking (NDN) is a novel network architecture which adopts a receiver-driven transport approach. However, NDN is the name-based routing and source uncontrollability, and network congestion is inevitable. In this paper, we propose an adaptive congestion control protocol (ACCP) which is divided into two phase to control network congestion before affecting network performance. In the first phase, we employ the time series prediction model based on deep learning to predict the source of congestion for each node. In the second phase, we estimate the level of network congestion by the average queue length based on the outcomes of first phase in each router and explicitly return it back to receiver, and then the receiver adjusts sending rate of Interest packets to realize congestion control. Simulation experiment results show that our proposed ACCP scheme has better performance than ICP and CHoPCoP in terms of the high utilization and minimal packet drop in a multi-source/multi-path environment.
Similar content being viewed by others
References
Fang C, Yu FR, Huang T, Liu J, Liu Y (2015) A survey of green information-centric networking: research issues and challenges. IEEE Commun Surv Tutor 8(3):1455–1472
Amadeo M, Campolo C, Quevedo J, Corujo D (2016) Information-centric networking for the internet of things: challenges and opportunities. IEEE Netw 30(2):92–100
Ahlgren B, Dannewitz C, Imbrenda C, Kutscher D (2012) A survey of information-centric networking. IEEE Commun Mag 50(7):26–36
Jacobson V, Smetters DK, Thornton J, Braynard R (2012) Networking named content. In: Communications of the ACM, vol 25, no 1, pp 1235–1248
Zhang M, Xie P, Zhu J, Wu Q, Zheng R, Zhang H (2017) NCPP-based caching and NUR-based resource allocation for information-centric networking. J Ambient Intell Humaniz Comput 8:1–7
Carofiglio G, Gallo M, Muscariello L (2012) Joint hop-by-hop and receiver-driven interest control protocol for content-centric networks. ACM SIGCOMM Comput Commun Rev 42(4):491–496
Saino L, Cocora C, Pavlou G (2013) CCTCP: a scalable receiver-driven congestion control protocol for content centric networking. In: IEEE international conference on communications, pp 3775–3780
Pacifici V, Dán G (2016) Coordinated selfish distributed caching for peering content-centric networks. IEEE/ACM Trans Netw 24(6):3690–3701
Li Q, Lee PPC, Zhang P, Su P, He L, Ren K (2017) Capability-based security enforcement in named data networking. IEEE/ACM Trans Netw 25(5):2719–2730
Karami A (2015) ACCPndn: adaptive congestion control protocol in named data networking. J Netw Comput Appl 56(1):1–18
Xu Q, Sun J (2014) A simple active queue management based on the prediction of the packet arrival rate. J Netw Comput Appl 42:12–20
Li W, Oteafy SMA, Hassanein HS (2017) Rate-selective caching for adaptive streaming over information-centric networks. IEEE Trans Comput 66(9):1613–1628
Matsuzono K, Asaeda H, Turletti T (2017) Low latency low loss streaming using in-network coding and caching. In: IEEE INFOCOM
Lv Y, Duan Y, Kang W, Li Z, Wang FY (2015) Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst 16(2):865–873
Munoz L, Mazon JN, Trujillo J (2011) ETL process modeling conceptual for data warehouses: a systematic mapping study. IEEE Latin Am Trans 3(9):358–363
Huang W, Song G, Hong H et al (2014) Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans Intell Transp Syst 15(5):2191–2201
Ndikumana A, Ullah S, Kamal R, Thar K, Kang HS, Moon SI, Hong CS (2015) Network-assisted congestion control for information centric networking. In: IEEE network operations and management symposium, pp 464–467
He Z, Zeng X, Qu S, Wu Z (2016) The time series prediction model based on integrated deep learning. J ShanDong Univ Eng Sci 46(6):40–47
Carofiglio G, Gallo M, Muscariello L (2012) ICP: design and evaluation of an interest control protocol for content-centric networking. In: IEEE computer communications workshops, pp 304–309
Salsano S, Detti A, Cancellieri M, Pomposini M, Blefari-Melazzi N (2012) Transport-layer issues in information centric networks. In: Edition of the ICN workshop on information-centric networking. ACM, pp 19–24
Saltarin J, Bourtsoulatze E, Thomos N, Braun T (2016) NetCodCCN: a network coding approach for content-centric networks. In: IEEE INFOCOM
Carofiglio G, Gallo M, Muscariello L, Papalini M, Wang S (2014) Optimal multipath congestion control and request forwarding in information-centric networks. IEEE Int Conf Netw Protoc 110:1–10
Ren Y, Li J, Shi S, Li L, Wang G (2016) An explicit congestion control algorithm for named data networking. In: Computer communications workshops. IEEE, pp 294–299
Zhang F, Zhang Y, Reznik A, Liu H, Qian C, Xu C (2014) A transport protocol for content-centric networking with explicit congestion control. In: IEEE international conference on computer communication and networks, pp 1–8
Zhou J, Wu Q, Li Z, Kaafar MA (2015) A proactive transport mechanism with explicit congestion notification for NDN. In: IEEE international conference on communications, pp 5242–5247
Taylor GW, Hinton GE, Roweis ST (2011) Two distributed-state models for generating high-dimensional time series. J Mach Learn Res 12(2):1025–1068
Yi C, Afanasyev A, Moiseenko I, Wang L, Zhang B, Zhang L (2013) A case for stateful forwarding plane. Comput Commun 36(7):779–791
Mastorakis S, Afanasyev A, Moiseenko I, Zhang L (2015) ndnSIM2.0: a new version of the NDN simulator for NS-3. Technical Report NDN-0028, NDN
Ding X, Canu S, Denoeux T (1995) Neural network based models for forecasting. In: Neural networks and their applications, pp 243–252
Acknowledgements
This work is partially supported by the National Natural Science Foundation of China (NSFC) under Grants Nos. U1604155, U1404611 and 61602155, in part by the Program for Science & Technology Innovation Talents in the University of Henan Province under Grants No. 16HASTIT035, and in part by Henan Science and Technology Innovation Project under Grant Nos. 164200510007 and 174100510010, and in part by the Industry University Research Project of Henan Province under Grant No. 172107000005, and in part by the support program for young backbone teachers in Henan Province under Grant no. 2015GGJS-047.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that there is no conflict of interest regarding the publication of this paper.
Rights and permissions
About this article
Cite this article
Liu, T., Zhang, M., Zhu, J. et al. ACCP: adaptive congestion control protocol in named data networking based on deep learning. Neural Comput & Applic 31, 4675–4683 (2019). https://doi.org/10.1007/s00521-018-3408-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-018-3408-2