simulation and analysis of loss in ip networks velibor markovski communication networks laboratory ...

SIMULATION AND ANALYSIS OF LOSS IN IP NETWORKS

Velibor Markovski

Communication Networks Laboratoryhttp://www.ensc.sfu.ca/research/cnl

School of Engineering ScienceSimon Fraser University

October 6, 2000 Simulation and analysis of loss in IP networks 2

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


QoS parameters for multimedia applications

Packet loss

Packet delay

Delay jitter

Packet delay

Pack

et

loss

Interactivevideo

Voice

Interactivedata


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.

Sources of packet loss


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


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)

sent

lost


Two-state Markov model (Gilbert model)

0 1

p01

p10

p11p00

State 0: successfully received packetState 1: lost packet


Extended Gilbert model

0 1p00

p01 p12

m-1 m

p(m-1)m

pm0

p(m-1)0

p10pmm

p(m-2)(m-1)

State 0: successfully received packetState i : i consecutively lost packets


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


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


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


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


Simulation scenario (2)

4 transit routers and 200 end hosts Mix of network traffic (Web, FTP, and

trace-driven video)

.. .R1 R4R3R2 DS

x = 100 Mbpsy = 1.5 - 10 Mbpsz = 22 - 32 Mbps

x yx

z z

z z

zz

.. .

.. .

.. .


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

Packet loss rate at the router buffer. Simulation run with 100 UDP sources and buffer size of 100 KB (18.3 msec).

),(

),()(

tTtsent

tTtlosttratelosspacket


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).


Definition of packet loss episodes

Loss episodeof length 3

Loss episodeof 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)


Packet loss episodes.Simulation run with n UDP sources and buffer size of 50 KB (9.2 msec).

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

(%) 100total

k

O

o



Contribution of loss episodes(UDP sources)

Increase of traffic load leads to : lengthier loss

episodes higher

contribution of lengthier loss episodes



Increase of traffic load leads to : lengthier loss

episodes higher

contribution of lengthier loss episodes




The contribution of single packet losses decreases with the increase of the traffic load



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







n k = 1 k = 2 k = 3

12088.4%

10.4%

1.0%

14093.4%

6.4% 0.2%

16093.3%

6.5% 0.2%


Packet loss rates.Simulation run with 80 UDP and 40 TCP sources,and buffer size of 50 KB (9.2 msec).

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


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).


Comparison of simulation datawith 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.


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


Analysis of packet loss on multiple time scales (1)

What is time scale? Wavelet analysis of packet loss

UDP scenario TCP scenario


Analysis of packet loss on multiple time scales (2)

Variance-time and R/S plots


Conclusions (1)

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

UDP transfers:


Conclusions (2)

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

UDP transfers:


Conclusions (3)

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%)

TCP transfers:


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


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.


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.


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