SIMULATION OF WIRELESS ESTIMATION BANDWIDTH FOR NETWORK TECHNOLOGY

Authors

  • WAN AEZWANI WAN ABU BAKAR
  • CHE MUHAMMAD FAIZUL CHE WAIL
  • MUSTAFA MAN

DOI:

https://doi.org/10.46754/jmsi.2022.06.001

Keywords:

Available Bandwidth, Wireless Network, Probe Rate Model, Active Bandwidth Estimation Techniques, Network Throughput

Abstract

Wireless estimation bandwidth is used in path selection for a network environment. To access the internet, a typical user shares the same bandwidth that has been provided by the Internet Service Provider (ISP). The scenario worsens when a few of the users access multimedia content on the Internet which requires high bandwidth at the same time. This research could be enhanced in other different network environments, such as wired and mobile phone environments, and represents an analysis of ready bandwidth using a few wireless bandwidth estimation tools, which are WBest, Pathload, IGI MRTG, PRTG and PTR. This paper attempts to design and develop a detection mechanism for wireless estimation bandwidth to estimate an available bandwidththrough several sending of injection traffic packets to the server. In this paper, we measured available bandwidth with WBest. Then we analysed the estimation measurements prior to 2 simulations done on Wireless Mode 11 Mbps 802.11b and Wireless Mode 54 Mbps 802.11g. Three different percentages of packet injection (0%, 25%, 50%) were tested on Wireless Mode 54 Mbps 802.11g. The results accuracy shows that the 50% packet injection performs the least available bandwidth compared to the injection of 0% and 25% of packets.

References

S., Banerji & R. S., Chowdhury. (2013). On IEEE 802.11: Wireless LAN Technology. arXiv preprint arXiv:1307.2661. International Journal of Mobile Network Communication & Telematic (IJMNCT), 3(4). DOI: https://doi.org/10.5121/ijmnct.2013.3405

M. X., Gong, B., Hart & S., Mao. (2015) Advanced wireless LAN technologies: IEEE 802.11 ac and beyond. GetMobile: Mobile Computing and Communications, 18(4), 48-52. DOI: https://doi.org/10.1145/2721914.2721933

C., Jinwei, K. M., McNeill, D., Zhang & J. F., Nunamaker. (2004). An overview of network-aware applications for mobile multimedia delivery. Proceedings of the 37th Annual Hawaii International Conference on System Sciences (pp-10). IEEE. doi: 10.1109/HICSS.2004.1265689 DOI: https://doi.org/10.1109/HICSS.2004.1265689

J. Hui & J. A. Smith. (2020). U.S. Patent No. 10,587,720. Washington, DC: U.S. Patent and Trademark Office.

O. Ayodeji, A., Aderounmu, O., Obembe & F., Ogwu. (2006). An efficient host-to-host available bandwidth estimation scheme.

A. Ferrari, M. Cantono, B. Mirkhanzadeh, Z. Lu, A. Shakeri, C. Shao, C., & V. Curri. (2018). A two-layer network solution for reliable and efficient host-to-host transfer of big data. 20th International Conference on Transparent Optical Networks (ICTON) (pp. 1-4). IEEE. DOI: https://doi.org/10.1109/ICTON.2018.8473597

R. Prasad, C. Dovrolis, M., Murray & K. C. Claffy. (2003). Bandwidth estimation: Metrics, measurement techniques and tools. IEEE Network, 17(6), pp. 27-35. DOI: https://doi.org/10.1109/MNET.2003.1248658

G. P. Jesi & G. Mazzini. (2020). Banda Calculus: A tool for bandwidth estimation in broadband network infrastructures. 28th International Conference on Software, Telecommunications and Computer Networks (SoftCOM) (pp. 1-5), IEEE. DOI: https://doi.org/10.23919/SoftCOM50211.2020.9238312

M. Li, M. Claypool & R. Kinicki. (2006). Packet dispersion in IEEE 802.11 wireless networks. Proceedings of 31st IEEE Conference on Local Computer Networks (pp. 721 – 729). DOI: https://doi.org/10.1109/LCN.2006.322028

T. Yang, Y. Jin & Y. Chen. (2017). RT-WABest: A novel end-to-end bandwidth estimation tool in IEEE 802.11 wireless network. International Journal of Distributed Sensor Networks, 13(2), 1550147717694889.

A. Shriram, M. Murray, Y. Hyun, N. Brownlee, A. Broido & M. Fomenkov. (2005). Comparison of public end-to-end bandwidth estimation tools on high-speed links. In International Workshop on Passive and Active Network Measurement (pp. 306-320), Berlin, Heidelberg: Springer. DOI: https://doi.org/10.1007/978-3-540-31966-5_24

T. Yang, J. Yuehui, C. Yufei & J. Yudong. (2017). RT-WABest: A novel end-to-end bandwidth estimation tool in IEEE 802.11 wireless network. International Journal of Distributed Sensor Networks, 13(2), 1550147717694889. DOI: https://doi.org/10.1177/1550147717694889

E. Goldoni & M. Schivi. (2020). End-to-end available bandwidth estimation tools, an experimental comparison. In International Workshop on Traffic Monitoring and Analysis (pp. 171-182). Berlin, Heidelberg: Springer. DOI: https://doi.org/10.1007/978-3-642-12365-8_13

C. D. Guerrero & M. A. Labrador. (2006). Experimental and analytical evaluation of available bandwidth estimation tools. Proceedings of 31st IEEE Conference on Local Computer Networks (pp. 710-717). IEEE. DOI: https://doi.org/10.1109/LCN.2006.322181

F. Ciaccia, I. Romero, O. Arcas-Abella, D. Montero, R. Serral-Gracià & M. Nemirovsky. (2020). Sabes: Statistical available bandwidth estimation from passive TCP measurements. IFIP Networking Conference (Networking) (pp. 743-748). IEEE.

A. Johnsson. (2005). Bandwidth measurements in wired and wireless networks. Department of Computer Science and Electronics, Mälardalen University.

W. Shi & G. Chen. (2021). Research on bandwidth allocation strategy of train communication network based on time division multiplexing. Procedia Computer Science, 183, (pp. 448-455). DOI: https://doi.org/10.1016/j.procs.2021.02.083

J. Strauss, D. Katabi & F. Kaashoek. (2003). A measurement study of available bandwidth estimation tools. Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement. (pp. 39-44). DOI: https://doi.org/10.1145/948205.948211

A. Tirumala, L. Cottrell & T. Dunigan. (2003). Measuring end-to-end bandwidth with Iperf using Web100. Proc. of Passive and Active Measurement Workshop (PAM). DOI: https://doi.org/10.2172/813039

K. Lakshminarayanan, V. N. Padmanabhan & J. Padhye. (2004). Bandwidth estimation in broadband access networks. In Proceedings of the 4th ACM SIGCOMM Conference on Internet Measurement (pp. 314-321). DOI: https://doi.org/10.1145/1028788.1028832

Published

30-06-2022