Abstract
The burst ratio parameter is used to describe the tendency of packet losses to occur in series, one after another. Such series of losses can be especially badly tolerated by some multimedia applications. The main reason of high values of the burst ratio are overflowed output buffers at network nodes. So far, the burst ratio was studied for the aggregated traffic only, i.e. for all the packets traversing the router’s output interface. In this paper, we study the burst ratio for every flow separately. We firstly compare the individual burst ratios with each other and with the burst ratio of the aggregated traffic, and identify conditions, under which they have the same values. Then we study the factors which may have an impact on the individual burst ratio, including the flow rate, variance of the interarrival time, packet service time distribution and buffer size.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
McGowan, J.W.: Burst ratio: a measure of Bursty loss on packet-based networks, 16 2005. US Patent 6,931,017 (2005)
Samociuk, D., et al.: Experimental measurements of the packet burst ratio parameter. Proc. BDAS 2018, 455–466 (2018)
Samociuk, D., et al.: Measuring and analyzing the burst ratio in IP traffic. In: Proceedings of the BROADNETS, pp. 86–101 (2019)
ITU-T Recommendation G.107: The E-model, a computational model for use in transmission planning. Technical report (2014)
Bolot, J.: End-to-end packet delay and loss behavior in the Internet. In: Proceedings of the ACM SIGCOMM1993, pp. 289–298 (1993)
Coates, M., Nowak, R.: Network loss inference using unicast end-to-end measurement. In: ITC Conference on IP Traffic, Measurement and Modeling (2000)
Duffield, N., et al.: Inferring link loss using striped unicast probes. IEEE INFOCOM, pp. 915–923 (2001)
Benko, P., Veres, A.: A passive method for estimating end-to-end TCP packet loss. IEEE GLOBECOM, pp. 2609–2613 (2002)
Sommers, J., et al.: Improving accuracy in end-to-end packet loss measurement. ACM SIGCOMM Comput. Commun. Rev. 35(4), 157–168 (2005)
Yajnik, M., et al.: Measurement and modelling of the temporal dependence in packet loss. Proc. IEEE INFOCOM 1, 345–352 (1999)
Sanneck, H.A., Carle, G.: Framework model for packet loss metrics based on loss run-lengths. SPIE Proc. 3969, 1–11 (2000)
Yu, X., et al.: The accuracy of Gilbert models in predicting packet-loss statistics for a single-multiplexer network model. In: IEEE INFOCOM, pp. 2602–2612 (2005)
Hasslinger, G., Hohlfeld, O.: The Gilbert-Elliott model for packet loss in real time services on the Internet. Proc. MMB 2008, 1–15 (2008)
Chydzinski, A., Wojcicki, R., Hryn, G.: On the number of losses in an MMPP queue. Lecture Notes in Computer Science, vol. 4712, pp. 38–48 (2007)
Chydzinski, A., Adamczyk, B.: Transient and stationary losses in a finite-buffer queue with batch arrivals. Math. Probl. Eng. 2012, 326830 (2012)
Rachwalski, J., Papir, Z.: Burst ratio in concatenated Markov-based channels. J. Telecommun. Inf. Technol. 1, 3–9 (2014)
Rachwalski, J., Papir, Z.: Analysis of burst ratio in concatenated channels. J. Telecommun. Inf. Technol. 4, 65–73 (2015)
Chydzinski, A., Samociuk, D.: Burst ratio in a single-server queue. Telecommun. Syst. 70(2), 263–276 (2019)
Chydzinski, A., et al.: Burst ratio in the finite-buffer queue with batch Poisson arrivals. Appl. Math. Comput. 330, 225–238 (2018)
Cidon, I., et al.: Analysis of packet loss processes in high-speed networks. IEEE Trans. Inf. Theory 39(1), 98–108 (1993)
Bratiychuk, M., Chydzinski, A.: On the loss process in a batch arrival queue. Appl. Math. Modell. 33(9), 3565–3577 (2009)
Internet Engineering Task Force. Request for Comments 7567. In: Baker, F., Fairhurst, G. (eds.) (2015)
Chrost, L., et al.: On the performance of AQM algorithms with small buffers. Commun. Comput. Inf. Sci. 39, 168–173 (2009)
Nichols, K., Jacobson, V.: Controlling queue delay. Queue 55(7), 42–50 (2012)
Khoshnevisan, L., Salmasi, F.R.: A robust and high-performance queue management controller for large round trip time networks. Int. J. Syst. Sci. 47(7), 1–12 (2016)
Chrost, L., Chydzinski, A.: On the deterministic approach to active queue management. Telecommun. Syst. 63(1), 27–44 (2016)
Wang, P., et al.: Active queue management algorithm based on data-driven predictive control. Telecommun. Syst. 64, 1–9 (2017)
Kahe, G., Jahangir, A.H.: A self-tuning controller for queuing delay regulation in TCP/AQM networks. Telecommun. Syst. 71(2), 215–229 (2019)
Chydzinski, A., Mrozowski, P.: Queues with dropping functions and general arrival processes. PLoS ONE 11(3), 1–23 (2016)
Barczyk, M., Chydzinski, A.: Experimental testing of the performance of packet dropping schemes. In: Proceedings of the IEEE ISCC, pp. 1–7 (2020)
Smolka, B., et al.: New filtering technique for the impulsive noise reduction in color images. Math. Probl. Eng. 2004(1), 79–91 (2004)
Acknowledgement
This work was conducted within project 2020/39/B/ST6/ 00224, founded by National Science Centre, Poland.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chydzinski, A., Adamczyk, B. (2022). Burst Ratios of Individual Flows. In: Rocha, A., Adeli, H., Dzemyda, G., Moreira, F. (eds) Information Systems and Technologies. WorldCIST 2022. Lecture Notes in Networks and Systems, vol 470. Springer, Cham. https://doi.org/10.1007/978-3-031-04829-6_33
Download citation
DOI: https://doi.org/10.1007/978-3-031-04829-6_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-04828-9
Online ISBN: 978-3-031-04829-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)