1. SIMULATION AND ANALYSIS OF LOSS IN IP NETWORKS
Velibor Markovski
Communication Networks Laboratory
School of Engineering Science
Simon Fraser University

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

8. Extended Gilbert model1
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

9. 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

10. 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

11. 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

12. Simulation and analysis of loss in IP networks
ns-2 network simulator
Collaborative project among USC, Xerox PARC, LBL, and UCB (
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

13. 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
Mbps
October 6, 2000
Simulation and analysis of loss in IP networks

14. 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

15. 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 = Mbps
z = Mbps x y z
October 6, 2000
Simulation and analysis of loss in IP networks

16. 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

17. 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

18. 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

19. 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

20. 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

21. 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

22. 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

23. 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

24. 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

25. 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

26. 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

27. 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

28. 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

29. 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

30. 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

31. 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

32. 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

33. 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

34. 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

35. 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

36. 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

37. 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

38. 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

39. 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 , 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 , 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 , 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 , 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

40. Thank you for your attention !
Questions ?
October 6, 2000
Simulation and analysis of loss in IP networks