Abstract
A large number of IoT devices are being used in the world. Data acquired by IoT devices are used as Big Data. These data sent from IoT devices to access points (AP) or base stations through wireless networks are stored in the cloud or data centers via wired networks such as back haul and backbone networks. The bandwidth of wireless or wired networks is oppressed by the transmission of a large amount of data. It causes network congestion, and the data cannot transmit and receive properly. To detect a sign of network congestion and prevent it is a very effective way to alleviate such congestion. However, it is difficult for us to predict a fluctuation of network traffic because it is caused by many factors and highly complex. Therefore, we try to predict it using Recurrent Neural Network (RNN), which exhibits dynamic temporal behavior for a time sequence in Deep Learning classes.
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References
Allman, M., Paxson, V., Blanton, E.: TCP Congestion Control, Internet RFC 5681, September 2009
Henderson, T., Floyd, S., Gurtov, A.: The NewReno modification to TCP’s fast recovery algorithm. Technical report, IETF (2004)
Xu, L., Harfoush, K., Rhee, I.: Binary increase congestion control for fast long-distance networks. In: Proceedings of INFOCOM, March 2004
Ha, S., Rhee, I., Xu, L.: CUBIC: a new TCP-friendly high-speed TCP variant. http://netsrv.csc.ncsu.edu/export/cubic_a_new_tcp_2008.pdf
Tan, K., Song, J., Zhang, Q., Sridharan, M.: A compound TCP approach for high-speed and long-distance networks. In: Proceedings of INFOCOM, April 2006
Cardwell, N., Cheng, Y., Gunn, C.S., Yeganeh, S.H., Jacobson, V.: BBR: congestion-based congestion control. Queue 14(5), 50 (2016). https://doi.org/10.1145/3012426.3022184. 34 pages
Sinha, P., Nandagopal, T., Venkitaraman, N., Sivakumar, R., Bharghavan, V.: WTCP: a reliable transport protocol for wireless wide-area networks. Wirel. Netw. 8(2/3), 301–316 (2002). Selected Papers from Mobicom 1999 Archive
Casetti, C., Geria, M., Mascolo, S., Sanadidi, M.Y., Wang, R.: TCP westwood: end-to-end congestion control for wired/wireless networks. Wirel. Netw. 8(5), 467–479 (2002)
Grieco, L.A., Mascolo, S.: Performance evaluation and comparison of Westwood+, New Reno, and Vegas TCP congestion control. ACM SIGCOMM Comput. Commun. Rev. 34(2), 25–38 (2004)
Hirai, H., Yamaguchi, S., Oguchi, M.: A proposal on cooperative transmission control middleware on a smartphone in a WLAN environment. In: Proceedings of IEEE WiMob 2013, pp. 701–717. IEEE (2013). http://ieeexplore.ieee.org/document/6673432/
Hayakawa, A., Yamaguchi, S., Oguchi, M.: Reducing the TCP ACK packet backlog at the WLAN access point. In: Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication (IMCOM 2015), Article 37, 8 p. ACM, New York (2015). https://doi.org/10.1145/2701126.2701164
Shimada, A., Yamaguchi, S., Oguchi, M.: Performance improvement of TCP communication based on cooperative congestion control in Android terminals. In: Proceedings of the 12th International Conference on Ubiquitous Information Management and Communication (IMCOM 2018) (2018)
Karnik, A., Kumar, A.: Performance of TCP congestion control with explicit rate feedback. IEEE/ACM Trans. Netw. (TON) 13(1), 108–120 (2005). https://ieeexplore.ieee.org/document/1402475/
Park, C., Woo, D.-M.: Prediction of network traffic by using dynamic bilinear recurrent neural network. IEEE (2009). Print ISBN 978-0-7695-3736-8
Chabaa, S., Zeroual, A., Antari, J.: Identification and prediction of internet traffic using artificial neural networks. Sci. Res. 2, 147–155 (2010)
Junsong, W., Jiukun, W., Maohua, Z., Junjie, W.: Prediction of internet traffic based on Elman neural network. IEEE (2009). Print ISBN 978-1-4244-2722-2
Joshi, M., Hadi, T.H.: A review of network traffic analysis and prediction techniques. arXiv preprint arXiv:1507.05722 (2015)
Mottini, A., Acuna-Agost, R.: Deep choice model using pointer networks for airline itinerary prediction. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1575–1583 (2017). https://doi.org/10.1145/3097983.3098005
Kannan, A., Kurach, K., Ravi, S., Kaufmann, T., Tomkins, A., Miklos, B., Corrado, G., Lukács, L., Ganea, M., Young, P., Ramavajjala, V.: Smart reply: automated response suggestion for email. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 955–964 (2016). https://doi.org/10.1145/2939672.2939801
Chen, Z., Gao, B., Zhang, H., Zhao, Z., Liu, H., Cai, D.: User personalized satisfaction prediction via multiple instance deep learning. In: Proceedings of the 26th International Conference on World Wide Web, pp. 907–915 (2017). https://doi.org/10.1145/3038912.3052599
Miki, K., Yamaguchi, S., Oguchi, M.: Kernel monitor of transport layer developed for Android working on mobile phone terminals. In: Proceedings of ICN 2011, pp. 297–302, January 2011
Miki, K., Yamaguchi, S., Oguchi, M.: Kernel monitor of transport layer developed for Android working on mobile phone terminals. In: Proceedings of the Tenth International Conference on Networks. ICN, pp. 297–302. https://doi.org/10.1109/WiMOB.2013.6673432
Riverbed Technology: Riverbed (1997). https://www.riverbed.com. Accessed 20 Sept 2018
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Yamamoto, A. et al. (2019). Prediction of Traffic Congestion on Wired and Wireless Networks Using RNN. In: Lee, S., Ismail, R., Choo, H. (eds) Proceedings of the 13th International Conference on Ubiquitous Information Management and Communication (IMCOM) 2019. IMCOM 2019. Advances in Intelligent Systems and Computing, vol 935. Springer, Cham. https://doi.org/10.1007/978-3-030-19063-7_26
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