Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 11

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

Available Bandwidth Estimation in Smart VPN Bonding Technique based on a NARX Neural Network

, , , , , ,

DOI: http://dx.doi.org/10.15439/2017F401

Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 601606 ()

Full text

Abstract. Today many applications require a high Quality of Service (QoS) to the network, especially for real time applications like VoIP services, video/audio conferences, video surveillance, high definition video transmission, etc. Besides, there are many application scenarios for which it is essential to guarantee high QoS in high speed mobility context using an Internet Mobile access. However, internet mobile networks are not designed to support the real-time data traffic due to many factors such as resource sharing, traffic congestion, radio link, coverage, etc., which affect the Quality of Experience (QoE). In order to improve the QoS in mobility scenarios, the authors propose a new technique named ``Smart VPN Bonding'' which is based on aggregation of two or more internet mobile accesses and is able to provide a higher end-to-end available bandwidth due to an adaptive load balancing algorithm. In this paper, in order to dynamically establish the correct load balancing weights of the smart VPN bonder, a neural network approach to predict the main Key Performance Indicators (KPIs) values in a determinate geographical point is proposed.


  1. Beritelli, F., La Corte, A., Rametta, C., Scaglione, F. (2015). A Cellular bonding and adaptive load balancing based multi-sim gateway for mobile ad hoc and sensor networks. International Journal on Ad Hoc Networking Systems (IJANS), 5(3).
  2. Beritelli, F., La Corte, A., Lo Sciuto, G., Rametta, C., Scaglione, F. (2016). Adaptive VPN Bonding Technique for Enhancing Dual-SIM Mobile Internet Access. In: Proceedings of the International Symposium for Young Scientists in Technology, Engineering and Mathematics - SYSTEM - Catania, Italy, September 27-29, 2015. p. 47-54.
  3. Beritelli F, Rametta C, Raspanti A, Russo M, Scaglione F, Spallina G (2016). An advanced QOS analysis and evaluation method for mobile internet access. International Journal of Wireless and Mobile Networks, 2016, vol. 8, p. 55-70.
  4. Liu, Liangwen, and Jipeng Zhou. "Ad hoc on-demand QoS routing based on bandwidth prediction (AQBP)." 2012 8thIEEE International Conference onWireless Communications, Networking and Mobile Computing (WiCOM), 2012.
  5. Salih, Yass K., Ong Hang See, and Salman Yussof. "A fuzzy predictive handover mechanism based on MIH links triggering in heterogeneous wireless networks." International Conference on Software and Computer Applications (ICSCA). Vol. 41. 2012.
  6. Miyim, A. M., Ismail, M., Nordin, R., Mahardhika, G. “Generic vertical handover prediction algorithm for 4G wireless networks”. In Space Science and Communication (IconSpace), 2013 IEEE International Conference on (pp. 307-312).
  7. Reddy, K. Suresh Kumar, D. Rajaveerappa, and S. KhadeejaBanu. "Bandwidth Map-TCP friendly rate control algorithm for improving QoS in streaming applications." 2013 Fourth International Conference on. Computing, Commu- nications and Networking Technologies (ICCCNT), 2013.
  8. Yao, Jun, Salil S. Kanhere, and Mahbub Hassan. "Improving QoS in high-speed mobility using bandwidth maps." IEEE Transactions on Mobile Computing 11.4 (2012): 603-617.
  9. Landa, Raul, et al. "Measuring the relationships between internet geography and rtt." Computer Communications and Networks (ICCCN), 2013 22nd International Conference on. IEEE, 2013.
  10. Iliya, S., Goodyer, E., Gongora, M., Shell, J., & Gow, J. "Optimized artificial neural network using differential evolution for prediction of RF power in VHF/UHF TV and GSM 900 bands for cognitive radio networks." Computational Intelligence (UKCI), 2014 14th UK Workshop on. IEEE, 2014.
  11. Iliya, S., Goodyer, E., Gow, J., Shell, J., & Gongora, M. "Application of Artificial Neural Network and Support Vector Regression in cognitive radio networks for RF power prediction using compact differential evolution algorithm." Computer Science and Information Systems (FedCSIS), 2015 Federated Conference on. IEEE, 2015.
  12. Chaudhari, Shilpa Shashikant, and Rajashekhar C. Biradar. "Available bandwidth prediction using wavelet neural network in mobile ad-hoc networks." Circuits, Communication, Control and Computing (I4C), 2014 International Conference on. IEEE, 2014.
  13. Chaudhari, Shilpa Shashikant, and Rajashekhar C. Biradar, "Resource prediction using wavelet neural network in mobile ad-hoc networks." Advances in Electronics, Computers and Communications (ICAECC), 2014 International Conference on. IEEE, 2014.
  14. Marszalek, Z., “Novel Recursive Fast Sort Algorithm” Infor- mation and Software Technologies - 22nd International Conference, ICIST 2016, Druskininkai, Lithuania, October 13-15, 2016, Proceedings, 2016, pp. 344-355.
  15. Strauss, J., Katabi, D., and Kaashoek, F., "A measurement study of available bandwidth estimation tools." Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement. ACM, 2003.
  16. Ribeiro, V. J., Riedi, R. H., Baraniuk, R. G., Navratil, J., Cottrell, L. “Pathchirp: Efficient available bandwidth estimation for network paths." Passive and active measurement workshop. 2003.
  17. Manish, J. and Dovrolis, C. "Pathload: A measurement tool for end-to-end available bandwidth." In Proceedings of Passive and Active Measurements (PAM) Workshop. 2002.
  18. Capizzi, G., Lo Sciuto, G., Napoli, C., Tramontana, E. “A multithread nested neural network architecture to model surface plasmon polaritons propagation.” 2016 Micromachines, 7 (7), art. no. 110.
  19. Bonanno, F., Capizzi, G., Lo Sciuto, G., “A neuro wavelet-based approach for short-term load forecasting in integrated generation systems.”2013 4th International Conference on Clean Electrical Power: Renewable Energy Resources Impact, ICCEP 2013, pp. 772-776.
  20. G. Capizzi, G. Lo Sciuto, C. Napoli, E. Tramontana and M. Woźniak, "Automatic classification of fruit defects based on co-occurrence matrix and neural networks," 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), Lodz, 2015, pp. 861-867.
  21. Capizzi, G., Lo Sciuto, G., Woźniak, M. and R. Damaševicius, “A Clustering Based System for Automated Oil Spill Detection by Satellite Remote Sensing,” 2016 International Conference on Artificial Intelligence and Soft Computing, ICAISC 2016: Artificial Intelligence and Soft Computing pp 613-623.
  22. Sroczyński, Z., “Actiontracking for Multi-platform Mobile Applications,” Software Engineering Trends and Techniques in Intelligent Systems, CSOC 2017, pp. 339-348.