KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Big Data Based Dynamic Flow Aggregation over 5G Network Slicing

  • Sun, Guolin (School of Computer Science and Engineering, University of Electronic Science and Technology of China) ;
  • Mareri, Bruce (School of Computer Science and Engineering, University of Electronic Science and Technology of China) ;
  • Liu, Guisong (School of Computer Science and Engineering, University of Electronic Science and Technology of China) ;
  • Fang, Xiufen (School of Mathematical Sciences, University of Electronic Science and Technology of China) ;
  • Jiang, Wei (German Research Center for Artificial Intelligence (DFKI GmbH))
  • Received : 2017.02.16
  • Accepted : 2017.06.01
  • Published : 2017.10.31
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Abstract

Today, smart grids, smart homes, smart water networks, and intelligent transportation, are infrastructure systems that connect our world more than we ever thought possible and are associated with a single concept, the Internet of Things (IoT). The number of devices connected to the IoT and hence the number of traffic flow increases continuously, as well as the emergence of new applications. Although cutting-edge hardware technology can be employed to achieve a fast implementation to handle this huge data streams, there will always be a limit on size of traffic supported by a given architecture. However, recent cloud-based big data technologies fortunately offer an ideal environment to handle this issue. Moreover, the ever-increasing high volume of traffic created on demand presents great challenges for flow management. As a solution, flow aggregation decreases the number of flows needed to be processed by the network. The previous works in the literature prove that most of aggregation strategies designed for smart grids aim at optimizing system operation performance. They consider a common identifier to aggregate traffic on each device, having its independent static aggregation policy. In this paper, we propose a dynamic approach to aggregate flows based on traffic characteristics and device preferences. Our algorithm runs on a big data platform to provide an end-to-end network visibility of flows, which performs high-speed and high-volume computations to identify the clusters of similar flows and aggregate massive number of mice flows into a few meta-flows. Compared with existing solutions, our approach dynamically aggregates large number of such small flows into fewer flows, based on traffic characteristics and access node preferences. Using this approach, we alleviate the problem of processing a large amount of micro flows, and also significantly improve the accuracy of meeting the access node QoS demands. We conducted experiments, using a dataset of up to 100,000 flows, and studied the performance of our algorithm analytically. The experimental results are presented to show the promising effectiveness and scalability of our proposed approach.

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References

  1. Y. Yuan and X. Zhao, "5G: vision, scenarios and enabling technologies," ZTE Communications, vol. 13, no. 1, pp. 3-10, Mar. 2015.
  2. Dolzer, Klaus, Wolfgang Payer, and Markus Eberspacher., "A simulation study on traffic aggregation in multi-service networks," in Proc. of the IEEE Conference on ATM on High Performance Switching and Routing, 2000.
  3. Li, Xu, Jaya Rao, and Hang Zhang, "Engineering Machine-to-Machine Traffic in 5G," IEEE Internet of Things Journal, vol. 3, no. 4, pp. 609-618, 2016. https://doi.org/10.1109/JIOT.2015.2477039
  4. Hu, Yan, Dah-Ming Chiu, and John CS Lui, "Entropy based adaptive flow aggregation," IEEE/ACM Transactions on Networking, vol. 17, no.3, pp. 698-711, 2009. https://doi.org/10.1109/TNET.2008.2002560
  5. Imran, Ali, Ahmed Zoha, and Adnan Abu-Dayya, "Challenges in 5G: how to empower SON with big data for enabling 5G," IEEE Network, vol.28, no. 6, pp. 27-33, 2014. https://doi.org/10.1109/MNET.2014.6963801
  6. Jardak C, Oldewurtel F. "Parallel processing of data from very large-scale wireless sensor networks," ACM International Symposium on High Performance Distributed Computing, HPDC 2010, Chicago, Illinois, USA, pp.787-794, Jun. 2010.
  7. Curtis A R, Mogul J C, Tourrilhes J, et al., "DevoFlow: scaling flow management for highperformance networks," in Proc. of ACM SIGCOMM 2011 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, Toronto, On, Canada, pp. 254-265, August. 2011.
  8. R.Trestian, G.-M. Muntean, and K. Katrinis, "MiceTrap: Scalable traffic engineering of datacenter mice flows using OpenFlow," in Proc. of the 2013 IEEE Symposium on Integrated Network Management (IFIP 2013), pp. 904-907, May 2013.
  9. Guo W, Mahendran V, Radhakrishnan S., "Achieving throughput fairness in smart grid using SDN-based flow aggregation and scheduling," in Proc. of IEEE International Conference on Wireless and Mobile Computing, networking and Communications, pp.1-7, 2016.
  10. Velan, Petr., "EventFlow: Network flow aggregation based on user actions," IEEE/IFIP Network Operations and Management Symposium (NOMS), Istanbul, Turkey, 2016.
  11. Khalili, Ramin, et al., "Reducing State of OpenFlow Switches in Mobile Core Networks by Flow Rule Aggregation," in Proc. of International Conference on Computer Communication and Networks 2016, pp.1-9, 2016.
  12. Sama, Malla Reddy, et al., "Reshaping the Mobile core network via function decomposition and network slicing for the 5G era," in Proc. of IEEE Wireless Communications and Networking Conference Workshops (WCNCW), 2016.
  13. Katsalis, Kostas, et al., "5G Architectural Design Patterns," in Proc. of 2016 IEEE International Conference on Communications Workshops (ICC), pp.32-37, 2016.
  14. Lee, GM, "Data Traffic Model in Machine to Machine Communications over 5G Network Slicing," in Proc. of the International Conference on Developments in eSystems Engineering (DeSE 2016), Liverpool, September 2016.
  15. A. A. Chandio, N. Tziritas, and C.-Z. Xu, "Big-data processing techniques and their challenges in transport domain," ZTE Communications, vol. 13, no. 1, pp. 50-59, Mar. 2015.
  16. R. Cibin, K. P. Sudheer, I. Chaubey, "Sensitivity and identifiability of stream flow generation parameters of the SWAT model," Hydrological Process., vol. 24, no. 9, pp. 1133-1148, Apr. 2010. https://doi.org/10.1002/hyp.7568
  17. Anjali P P, Binu A. "Network Traffic Analysis: Hadoop Pig vs Typical MapReduce," Computer Science & Information Technology, 2013.
  18. Open Networking Foundation, OpenFlow Switch Specification Version 1.1.0 [White paper] Dec. 2011.
  19. Smith B L, Williams B M, Oswald R K., ''Comparison of parametric and nonparametric models for traffic flow forecasting,'' Elsevier Transportation Research Part C: Emerging Technologies, vol. 10, no. 4, pp. 303-321, Aug. 2002. https://doi.org/10.1016/S0968-090X(02)00009-8
  20. J. Zhang, Y. Xiang, Y. Wang, W. Zhou, Y. Xiang, and Y. Guan, ''Network traffic classification using correlation information,'' IEEE Trans. Parallel Distribution System, vol. 24, no. 1, pp. 104-117, Jan. 2013. https://doi.org/10.1109/TPDS.2012.98
  21. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification., New York, NY, USA: Wiley, 2012.
  22. P.-N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining. New York, NY, USA: Pearson Education Inc., 2006.
  23. Z. Zheng and D. Su, ''Short-term traffic volume forecasting: A k-nearest neighbor approach enhanced by constrained linearly sewing principle component algorithm,'' Elsevier Transportation Research Part C: Emerging Technologies, vol. 43, pp. 143-157, Jun. 2014. https://doi.org/10.1016/j.trc.2014.02.009
  24. Ranger C, Raghuraman R, Penmetsa A, et al., ''Evaluating MapReduce for multi-core and multiprocessor systems,'' in Proc. of IEEE International Symposium on High PERFORMANCE Computer Architecture, Scottsdale, AZ, USA, pp. 13-24, Feb. 2007.
  25. C.-S. Li and M.-C. Chen, ''A data mining based approach for travel time prediction in freeway with non-recurrent congestion,'' Neurocomputing, vol. 133, pp. 74-83, Jun. 2014. https://doi.org/10.1016/j.neucom.2013.11.029