Nonlinear Complex Behaviour of TCP in UMTS Networks and

Performance Analysis

I.Vasalos, Professor R.A.Carrasco,

Doctor W.L.Woo

Presentation Outline

Introduction Problem Statement Research Objectives Background Theory Methodology and Discussion Conclusions

Introduction

Evolution of communications Increase in the number of users

Mobile network connectivity

Network Architecture

Interoperation of wired and wireless networks

Difficulty in interoperation Different functional characteristics

Different performance limits

Complex voluminous applications: Internet, E-mail, FTP, Video

Strict QoS limits

• Powerful Machines• High Bandwidth Links• Rare Disconnections

• Rare Link Failures• Bandwidth of wireless media is limited by the available radio spectrum.

• The wireless bandwidth cannot be expanded infinitely.• Most mobile applications are asymmetric .

Problem Background

2G, 3GWLAN

…

InternetCloud

Wired Network

• Channel fading, multiple path, building blocking, …

• Needs mobility management• Rapid degradation to the delivered service quality due to wireless medium

• Traditional voice/paging• Increasing demand for data services (ftp, mail, Internet,…)• Real Time Multimedia Content (VoIP, Video)• Increased QoS Support

• Restricted Wireless Bandwidth• Error-prone, Time-varying Link• Mobility and Handoff• Service diversity

During congestion periods chaos and persistent oscillations appear in the traffic profile of the network. (Carrasco, [1])

This traffic behaviour is induced by the chaotic nature of TCP Congestion Control. (Veres [2])

In UMTS, chaotic TCP and the wireless nature of the network are expected to introduce more sensitivity and unpredictability in the network.

Limited understanding of TCP dynamics developed inside the UMTS network, if they are chaotic and what is their impact on network performance.

Problem Statement

Research Objective

“Simulation study of the UMTS mobile network under heavy traffic load. Identification of

dynamical behaviour inside the traffic profile of the network. Determination of the impact of

chaotic behaviour in the network performance.”

QoS and network fairness are radically altered. Under strong chaotic conditions the system experiences very high variations in the user throughput and unacceptably large delays.

Test the UMTS network under heavy traffic load in order to take chaotic and conventional QoS measurements and evaluate the performance of the network.Evidence that the network shows aperiodicity and sensitivity to initial conditions which are fundamental chaotic characteristics.

Work Contributions

Simulation study of the TCP’s dynamical behaviour in UMTS

network

Chaotic Behavior of TCP in UMTS NetworkChaotic Behavior of

TCP in UMTS NetworkEffect of chaos in QoS of network behavior

Effect of chaos in QoS of network behavior

UMTS Architecture

Third Generation mobile network evolution

Improved data communication including the delivery of multimedia

and real time services

Capacity of data rates of up to 144 kbits/sec in rural areas and

2Mbits/sec in indoor scenarios

Packet DataNetwork

UMTS Core Network

Circuit-SwitchedNetwork

UTRAN

TCP

End-to-end In order delivery of a packet stream

Packet retransmission and acknowledgments (ACKs)

Flow control: Use bandwidth efficiently

Prevent overflow of the receiver buffer

Congestion Control Sender does not overrun the available bandwidth

Prevent intermediate nodes become overloaded

“TCP provides a packet switched, connection-oriented, reliable byte stream service” [3]

Flow-Congestion Control

Keep the network operating at full capacity, in order to

minimize packet loss and maximize “goodput”

Accomplishment by two windows: Congestion window,

(cwnd) and Advertised window Window = min {Advertised window, cwnd}

“cwnd” follows additive increase/multiplicative decrease

(AIMD) On receipt of Ack: cwnd += 1

On packet loss (timeout): cwnd *= 0.5

CHAOS

“Aperiodic, Long-Term Behaviour of a Bounded system that exhibits sensitive dependence on initial conditions” [5]

Nonlinearity The system is governed by Nonlinear Equations.

Determinism The future is uniquely determined by the past according

to some rule or mathematical formula.

Aperiodicity A state or condition characterized by non regular

repetition in time or space

Sensitivity to Initial Conditions Same initial conditions lead to same final state… but the

final state is very different for small changes to initial conditions.

Experimental Setup

Construction of a typical UMTS mobile network

using OPNET modeller.

Simulation of the Network under a mobile traffic

application that uses the TCP protocol (FTP). For all the simulation we use TCP flavour “Reno”

Performance of the Mobile Station is evaluated

by measuring each TCP’s Congestion Window

[2]. “cwnd is related to the nonlinear equations that govern

the TCP data rate and congestion avoidance.”

Aperiodicity Experimental Setup

2 Mobile Stations send 2MB on the uplink to the UMTS mobile

network

Artificially simulate the network under heavy traffic load.

Decrease the network throughput capacity 20 (kbits/sec)

Test under different congestion levels thus we decrease the

router buffer size to 25 and 15 packets

Performance and System measures

Phase Space Graph It is the space in which all possible states of a system are represented, with each possible state of the system corresponding to one unique point in the phase space. From the time series of the “cwnd” we use (n) time shifted

past values of the “cwnd “ to average the value of the congestion window in order to reconstruct the underlying

multidimensional trajectories on the 2D plane [2]

Average user throughput

Total averaged throughput of the traffic sent on the uplink for the mobile station in Kbits/sec.

n

jx jicwnd

nix

1

][1

][

TCP Congestion Window

For buffer space 25 packets the values of Congestion Windows of the 2 Mobile Stations are recorded in relation to time.

Both mobiles show normal TCP behaviour of slow start, congestion avoidance, packet loss and back-off. The mobiles are synchronized in the way they start and stop packet transmission.

Co

ng

est

ion

Win

do

w

(kb

its)

50.000

40.000

30.000

20.000

10.000

0 5m 10m 15m 20m 25m 30m 35m

0

Time (min)

Co

ng

est

ion

Win

do

w

(kb

its)

50.000

40.000

30.000

20.000

10.000

0 5m 10m 15m 20m 25m 30m 35m

0

Time (min)

System is periodic as it goes through a loop returning to the same values creating a closed periodical trail.

Impact on the throughput : Both User Equipment (UE) share the resources of the network fairly. On average using 9 (kbits/sec)

TCP Periodic Behaviour

Phase Space graph and User Throughput for buffer size of 25 packets

Time (min)

0 5m 10m 15m 20m 25m 30m 35m

1.000

2.000

3.000

4.000

5.000

6.000

7.000

8.000

9.000

0

10.000

Aver

age

Thro

ughp

ut (

Kbits

/sec

)

UE1

UE 2

TCP Chaotic Aperiodic Behaviour

The mobiles send data in a very unsystematic way Extremely complex aperiodic graph, hence chaotic behaviour

Large unfairness between throughputs, 2.5 (kbits/sec)

0 5m 10m 15m 20m 25m 30m 35m

2.500

5.000

7.500

10.000

12.500

Aver

age

Thro

ughp

ut (

Kbits

/sec

)Time (min)

0

Phase Space graph and User Throughput for buffer size of 15 packets

Unfairness of 2.5 (kbits/sec)

UE1

UE 2

Increase the number of Mobile Stations, Remove any restrictions Perform simulation of the network Repeat exact simulation changing a parameter

Drop one packet randomly from one of the TCP sessions Repeat the experiment for 10, 20 and 30 Mobile Stations

Sensitivity to Initial Conditions

Performance and System measures

Spatiotemporal Graph Graph in which the value of each congestion window for all

mobile stations is represented according to a certain color.

Lyapunov Exponent measure of the system’s sensitive dependence to initial

conditions.

Euclidean Distance:

Lyapunov Exponent:

is the time it takes for , is the threshold value of the Euclidean distance, is the distance of the initial perturbation

In the simulation we use and

i

N

i

perunp

ttE

tit

tixtixtE

)(

ln1

),(

),(),()(

00

1

2

t ^

0 )( EttE ^

E

i10

^

E 1i

Time (sec)

Spatiotemporal Graph

Congestion Windows

of unperturbed system

Congestion Windows

of perturbed System

Difference of Systems

The system is not stable

The packet loss disturbs the dynamic of the Network

Hence we have sensitivity to Initial Conditions

Initially the systems look identical

On the packet drop a few differences appear

As time evolves the system looks completely different

Lyapunov exponents are a measure of the average rate of divergence

of neighboring trajectories of a system. System can be considered as chaotic if it has positive

Lyapunov exponents. Most sensitive system to the perturbation is the system with

the most users.

Largest Lyapunov Exponent of 30 Mobile Stations

Lyapunov Exponent

Lyapunov Exponent of 20 Mobile StationsLyapunov Exponent of 10 Mobile Stations

Time (sec)

λ

Impact on QoS

Under medium traffic loads (10 users, λ=0,2) the network is fairly stable

Under increased traffic loads (30 users λ=1,2) large unfairness is observed around 80 (kbits/sec)

Strong relation to the level of instability in the network with the level

of unfairness in the network

TCP Delay for 10 and 30 users is 90 and 500 secs relatively

Unfairness of 80 (kbits/sec)

Ave

rage

Thr

ough

put (

bits

/sec

)

Time (min)

10 Users Node-B 1

Node-B 2

0 5m 10m

100.000

0

200.000

300.000

400.000

Aver

age

Thro

ughp

ut (

Kbits

/sec

)

Time (min)

30 Users Node-B 1

Node-B 2

Unfairness of 80 (kbits/sec)

Performance of HTTP

In order to have a complete image of the network behaviour long-lived

flows (FTP) should be tested along with short-lived flows (HTTP).

Repeat the simulation for 10 and 30 MS using a traffic mix of 70% FTP

and 30% HTTP.

Measure the page delay time: The time it takes the user to retrieve

entire web-page with all the inline objects.

0

2

4

6

8

10

12

0 100 200 300 400 500 600 700

Time (sec)

UE 10UE 30

0

2

4

6

8

10

12

0 100 200 300 400 500 600 700

Time (sec)

Dela

y

UE 10UE 30

0

2

4

6

8

10

12

0 100 200 300 400 500 600 700

Time (sec)

UE 10UE 30

0

2

4

6

8

10

12

0 100 200 300 400 500 600 700

Time (sec)

Dela

y

UE 10UE 30

For 30 MS some users face double the delay time in comparison to 10 MS.

Due to the instability in the Network some HTTP browsers cannot finish the download of the page since some of the short lived TCP flows get severely delayed.

Conclusions

Mobile networks have seen a tremendous growth in the past decade and still keep expanding at a fast pace.

Mobile network can create chaos when certain traffic levels are exceeded.

It is hoped that during this research chaotic phenomena in mobile networks can be suppressed and the QoS under traffic congestion remain at high levels

REFERENCES

1. Greenwood D.P.A, Carrasco R.A.: ‘Neural Networks for the Adaptive Control of Disruptive Nonlinear Network Traffic.’ IEE Proc. Commun. 2000

2. Veres A, Boda M, ‘The Chaotic Nature of TCP Congestion Control’, IEEE INFOCOM’2000, March 2000

3. Chakravorty R, Cartwright J, Pratt I: ‘Practical experience with TCP over GPRS’ IEEE Global Telecommunications Conference, vol.2, 2002, pp.1678-82 vol.2.

4. Pointon C.T, Carrasco R.A and Gell M.A: ‘ Complex Behaviour in Nonlinear Systems’ , Modelling Future Telecommunications Systems, BT Telecommunications Series, Chapman & Hall, 1996, pp.311-344

5. KATHLEEN T.ALLIGOOD and TIM D SAUER.: Chaos an Introduction to Dynamical Systems. (Springer-Verlag New York 1996)