SIMULATION AND ANALYSIS OF LOSS IN IP NETWORKS Velibor Markovski Communication Networks Laboratory http://www.ensc.sfu.ca/research/cnl School of Engineering Science Simon Fraser University

Simulation and analysis of loss in IP networks Road map Motivation for packet loss analysis Sources of packet loss in the Internet Packet loss characterization Methodology for packet loss collection Simulation scenarios Simulation results Conclusions and future work October 6, 2000 Simulation and analysis of loss in IP networks

QoS parameters for multimedia applications Packet loss Packet delay Delay jitter Interactive data Packet loss Voice Interactive video Packet delay October 6, 2000 Simulation and analysis of loss in IP networks

Simulation and analysis of loss in IP networks Sources of packet loss At the end hosts In the routers (buffer overflow) On the links (fading on wireless links) Buffer overflow accounts for over 99% of all the lost packets in wireline networks. October 6, 2000 Simulation and analysis of loss in IP networks

Packet loss characterization Unconditional and conditional loss probability Two-state Markov model (Gilbert model) Extended Gilbert model General Markov chain model Heavy-tailed distribution of packet loss October 6, 2000 Simulation and analysis of loss in IP networks

Unconditional and conditional loss probability Unconditional loss probability or packet loss rate: ulp = P (packet n is lost) = Conditional loss probability clp = P (packet n+1 is lost | packet n is lost) October 6, 2000 Simulation and analysis of loss in IP networks

Two-state Markov model (Gilbert model) 1 p01 p10 p11 p00 State 0: successfully received packet State 1: lost packet October 6, 2000 Simulation and analysis of loss in IP networks

Extended Gilbert model 1 p00 p01 p12 m-1 m p(m-1)m pm0 p(m-1)0 p10 pmm p(m-2)(m-1) State 0: successfully received packet State i : i consecutively lost packets October 6, 2000 Simulation and analysis of loss in IP networks

General Markov chain model P(Xn=0 | Xn-1=0, Xn-2=0) Next event depends on the past n events, regardless of whether these events were losses or non-losses. 00 01 11 10 P(Xn=1 | Xn-1=1, Xn-2=0) October 6, 2000 Simulation and analysis of loss in IP networks

Heavy-tailed distribution of packet loss Pareto distribution to model the distribution of loss episode lengths. October 6, 2000 Simulation and analysis of loss in IP networks

Collection of packet loss data Passive measurements on live networks Active measurements on live networks Packet loss collection using simulation access to all data (enqued, dequed or dropped), at each network node flexibility in choosing the parameters October 6, 2000 Simulation and analysis of loss in IP networks

Simulation and analysis of loss in IP networks ns-2 network simulator Collaborative project among USC, Xerox PARC, LBL, and UCB (http://www.isi.edu/nsnam/ns/) Discrete event network simulator Open code Provides support for various: network protocols topologies traffic generators queue management and packet scheduling techniques October 6, 2000 Simulation and analysis of loss in IP networks

Simulation scenario (1) n video sources, a router, and a sink Droptail queue with buffer size set according to delay requirements Trace-driven simulation using genuine video traffic trace (Star Wars and Talk show) Three subscenarios: all sources use User Datagram Protocol all sources use Transmission Control Protocol mixed UDP/TCP traffic 1 2 3 n R D . 10 Mbps 44.736 Mbps October 6, 2000 Simulation and analysis of loss in IP networks

Source traffic – Starwars Trace Trace-driven simulation 170,000 frames (2 hours) Each source starts at a random point within the trace If the end of the trace is reached, the source reads from the beginning of the trace October 6, 2000 Simulation and analysis of loss in IP networks

Simulation scenario (2) 4 transit routers and 200 end hosts Mix of network traffic (Web, FTP, and trace-driven video) . R1 R4 R3 R2 D S x = 100 Mbps y = 1.5 - 10 Mbps z = 22 - 32 Mbps x y z October 6, 2000 Simulation and analysis of loss in IP networks

Simulation and analysis of loss in IP networks Packet loss rates Packet loss rate calculated over bins of size T: Long-term packet loss rate (ulp) is not enough to describe the loss process Aggregate loss at the router buffer. Simple topology, UDP sources. Packet loss rate at the router buffer. Simulation run with 100 UDP sources and buffer size of 100 KB (18.3 msec). October 6, 2000 Simulation and analysis of loss in IP networks

Textured dot strip plot Time instances of packet loss at the router buffer. Simulation run with 80 UDP sources and buffer size of 100 KB (18.3 msec). October 6, 2000 Simulation and analysis of loss in IP networks

Definition of packet loss episodes of length 3 of length 2 Successfully received packet Dropped packet n n+1 n+2 n+3 n+4 n+5 n+6 n+7 n+8 Loss distance = 3 (n+6) – (n+3) October 6, 2000 Simulation and analysis of loss in IP networks

Contribution of loss episodes Contribution of loss episode of length k: ok = number of loss episodes of length k Ototal = total number of loss episodes What is contribution of loss episodes? Define it. Packet loss episodes. Simulation run with n UDP sources and buffer size of 50 KB (9.2 msec). October 6, 2000 Simulation and analysis of loss in IP networks

Contribution of loss episodes (UDP sources) Increase of traffic load leads to : lengthier loss episodes higher contribution of lengthier loss episodes What is contribution of loss episodes? Define it. Packet loss episodes. Simulation run with n UDP sources and buffer size of 50 KB (9.2 msec). October 6, 2000 Simulation and analysis of loss in IP networks

Contribution of loss episodes (UDP sources) Increase of traffic load leads to : lengthier loss episodes higher contribution of lengthier loss episodes Packet loss episodes. Simulation run with n UDP sources and buffer size of 50 KB (9.2 msec). October 6, 2000 Simulation and analysis of loss in IP networks

Contribution of loss episodes (UDP sources) The contribution of single packet losses decreases with the increase of the traffic load Packet loss episodes. Simulation run with n UDP sources and buffer size of 50 KB (9.2 msec). October 6, 2000 Simulation and analysis of loss in IP networks

Contribution of loss episodes (TCP sources) Faster decrease of the packet loss episode contribution than in the UDP case Packet loss episodes of length 1 (single losses) contribute with more than 90% of all the loss episodes Packet loss episodes. Simulation run with n UDP sources and buffer size of 50 KB (9.2 msec). October 6, 2000 Simulation and analysis of loss in IP networks

Contribution of loss episodes (TCP sources) Faster decrease of the packet loss episode contribution than in the UDP case Packet loss episodes of length 1 (single losses) contribute with more than 90% of all the loss episodes Packet loss episodes. Simulation run with n UDP sources and buffer size of 50 KB (9.2 msec). October 6, 2000 Simulation and analysis of loss in IP networks

Contribution of loss episodes (TCP sources) k = 1 k = 2 k = 3 120 88.4% 10.4% 1.0% 140 93.4% 6.4% 0.2% 160 93.3% 6.5% Faster decrease of the packet loss episode contribution than in the UDP case Packet loss episodes of length 1 (single losses) contribute with more than 90% of all the loss episodes Packet loss episodes. Simulation run with n UDP sources and buffer size of 50 KB (9.2 msec). October 6, 2000 Simulation and analysis of loss in IP networks

Packet loss rates (Mixed UDP and TCP sources) Packet loss rate for the UDP sources is much larger than the packet loss rate for the TCP sources Packet loss rates. Simulation run with 80 UDP and 40 TCP sources, and buffer size of 50 KB (9.2 msec). October 6, 2000 Simulation and analysis of loss in IP networks

Contribution of loss episodes (Mixed UDP and TCP sources) Larger number of UDP sources leads to larger contribution of longer loss episodes Packet loss episodes. Simulation run with n UDP sources and 120-n, and buffer size of 50 KB (9.2 msec). October 6, 2000 Simulation and analysis of loss in IP networks

Reason for shorter loss episodes in TCP transfers Congestion window decreases upon occurrence of packet loss October 6, 2000 Simulation and analysis of loss in IP networks

Two-state Markov model (Gilbert model) 1 p01 p10 p11 p00 State 0: successfully received packet State 1: lost packet October 6, 2000 Simulation and analysis of loss in IP networks

Comparison of simulation data with the Gilbert models ulp = 0.15 clp = 0.45 Gilbert model fits the simulation data for small loss episodes Gilbert model underestimates the probability of having longer loss episodes Simulation run with 100 UDP sources and buffer size of 50 KB (9.2 msec). The loss from source number 50 is observed. October 6, 2000 Simulation and analysis of loss in IP networks

Contribution of loss episodes (complex topology) The contribution of loss episodes for the complex topology shows similar behavior as the simple topology, for both the aggregate and per-flow packet loss October 6, 2000 Simulation and analysis of loss in IP networks

Analysis of packet loss on multiple time scales (1) What is time scale? Wavelet analysis of packet loss My results and the collected packet loss sets have been used to perform further analysis of packet loss on multiple time scales UDP scenario TCP scenario October 6, 2000 Simulation and analysis of loss in IP networks

Analysis of packet loss on multiple time scales (2) Variance-time and R/S plots October 6, 2000 Simulation and analysis of loss in IP networks

Simulation and analysis of loss in IP networks Conclusions (1) UDP transfers: Lengthier packet loss episodes have large contribution, which indicates that UDP packet loss is highly bursty Contribution of packet loss episodes decreases approximately geometrically with increase of the length of packet loss episode October 6, 2000 Simulation and analysis of loss in IP networks

Simulation and analysis of loss in IP networks Conclusions (2) UDP transfers: Gilbert model is a good fit for short packet loss episodes, but underestimates the probability of having lengthier packet loss episodes Extended Gilbert model of order m tracks the packet loss episode exactly up to length m-1 UDP packet loss shows long-range dependent properties for coarser time scales October 6, 2000 Simulation and analysis of loss in IP networks

Simulation and analysis of loss in IP networks Conclusions (3) TCP transfers: Lower packet loss rates than UDP due to the congestion control mechanisms in TCP sources Short packet loss episodes (loss episodes of length one contribute with over 90%) October 6, 2000 Simulation and analysis of loss in IP networks

Simulation and analysis of loss in IP networks Future work Simulation and analysis of packet delay Impact of various queue management policies on packet loss patterns Impact of consecutive packet losses on end-user perception October 6, 2000 Simulation and analysis of loss in IP networks

Simulation and analysis of loss in IP networks References Velibor Markovski and Ljiljana Trajković, “Analysis of loss episodes for video transfers over UDP,” Proceedings of Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS 2000), Vancouver, BC, Canada, July 2000, pp. 278 – 285. Fei Xue, Velibor Markovski, and Ljiljana Trajković, “Wavelet analysis of packet loss for video transfers over UDP,” Proceedings of First International Conference on Internet Computing (IC 2000), Las Vegas, NV, USA, June 2000, pp. 427 – 433. October 6, 2000 Simulation and analysis of loss in IP networks

Simulation and analysis of loss in IP networks References Van Jacobson. Congestion avoidance and control. In Proceedings of ACM SIGCOMM '88 Symposium on Communications Architectures and Protocols, pages 314-329, Stanford, CA, USA, August 1988. Jean-Chrysostome Bolot. End-to-end packet delay and loss behavior in the Internet. In Proceedings of ACM SIGCOMM '93 Conference on Communications Architectures, Protocols and Applications, pages 289-298, San Francisco, CA, USA,September 1993. Henning Sanneck and Georg Carle. A framework model for packet loss metrics based on loss runlengths. In Proceedings of the SPIE/ACM SIGMM Multimedia Computing and Networking Conference 2000 (MMCN 2000), pages 177-187, San Jose, CA, USA, January 2000. Maya Yajnik, Sue Moon Jim Kurose, and Don Towsley. Measurement and modeling of the temporal dependence in packet loss. In Proceedings of IEEE INFOCOM, pages 345-352, New York, NY, USA, March 1999. Michael S. Borella and Debbie Swider. Internet packet loss: Measurement and implications for end-to-end QoS. In Proceedings of the 1998 ICPP workshops on architectural and OS support for multimedia applications/flexible communication systems/wireless networks and mobile computing, pages 3-12, Minneapolis, MN, USA, August 1998. October 6, 2000 Simulation and analysis of loss in IP networks

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