Transcript Nonlinear Complex Behaviour of TCP in UMTS Networks and

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
Problem Background
Wired Network
2G, 3G
WLAN
…
•
•
•
•
Restricted Wireless Bandwidth
Error-prone, Time-varying Link
Mobility and Handoff
Service diversity
•
Internet
Cloud
• Powerful Machines
• High Bandwidth Links
• Rare Disconnections
• Rare Link Failures
Bandwidth
of voice/paging
wireless media is limited by the available radio
• Traditional
• Channel
fading,
building blocking, …
•
Needsmultiple
mobilitypath,
management
spectrum.
• Increasing demand for data services (ftp, mail, Internet,…)
• Rapid degradation
to theexpanded
deliveredinfinitely.
service
• The
wireless
• Real
Time bandwidth
Multimediacannot
Contentbe(VoIP,
Video)
quality
due to wireless medium
• Most
mobile
applications
• Increased
QoS Supportare asymmetric .
Problem Statement
 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.
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.”
Work Contributions
Simulation study of the TCP’s
dynamical behaviour in UMTS network
Chaotic Behavior of
TCP in UMTS Network
Effect of chaos in QoS
of network behavior
QoS
and
network
fairness
areheavy
radically
altered.
Test
the
UMTS
network
under
traffic
load inUnder
orderstrong
to take
chaotic
conditions
the system
experiences
very
variations
Evidence
that
the network
shows
aperiodicity
andhigh
sensitivity
chaotic
and
conventional
QoS
measurements
and
evaluate
theto
in the
userof
throughput
and
unacceptably
largecharacteristics.
delays.
initial
conditions
which
are
fundamental
chaotic
performance
the
network.
UMTS Architecture
Circuit-Switched
Network
UTRAN
UMTS Core
Network
Packet Data
Network

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
TCP
“TCP provides a packet switched, connectionoriented, reliable byte stream service” [3]



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
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


Determinism


The future is uniquely determined by the past according to some
rule or mathematical formula.
Aperiodicity


The system is governed by Nonlinear Equations.
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]
1 n
x[i]   cwndx [i  j ]
n j 1

Average user throughput
Total averaged throughput of the traffic sent on the uplink for the
mobile station in Kbits/sec.

TCP Congestion Window

For buffer space 25 packets the values of Congestion Windows of the
2 Mobile Stations are recorded in relation to time.
W in d o w (k b its )
C o n g e s tio n
5 0 .0 0 0
4 0 .0 0 0
3 0 .0 0 0
2 0 .0 0 0
1 0 .0 0 0
0
0
5m
10m
15 m
20m
25m
30m
35m
Time (min)
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.

TCP Periodic Behaviour
Phase Space graph and User Throughput for buffer size of 25 packets
Average Throughput (Kbits/sec)

10.000
9.000
8.000
7.000
6.000
5.000
4.000
3.000
2.000

1.000
UE1
UE
0
0
5m
10m
15m
20m
25m
30m
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)
2
35m
TCP Chaotic Aperiodic Behaviour

Phase Space graph and User Throughput for buffer size of 15 packets
Average Throughput (Kbits/sec)
12.500
10.000
7.500
5.000
Unfairness of
2.5 (kbits/sec)
2.500

UE
0
0
5m
10m
15m
20m
Time (min)

The mobiles send data in a very unsystematic way

Extremely complex aperiodic graph, hence chaotic behaviour

UE1
Large unfairness between throughputs, 2.5 (kbits/sec)
25m
30m
2
35m
Sensitivity to Initial Conditions




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
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.
 x
Euclidean Distance:
E (t ) 
Lyapunov Exponent:
 (t0 , i ) 
N
unp
(i, t )  x
per
(i, t ) 
2
i 1
E (t0  t )
1
ln
t
i
^

t is the time it takes for

In the simulation we use E  10 and  i
^
E (t0  t )  E , E is the threshold value of
the Euclidean distance,  i is the distance of the initial perturbation
^
1
Spatiotemporal Graph
Congestion Windows
of unperturbed system
Congestion Windows
of perturbed System
Difference of Systems
Time (sec)
Initially the systems
As time
On the packet drop
a fewevolves the
look

Theidentical
system
is not appear
stablesystem looks
differences
completely different
 The packet loss disturbs the dynamic of the Network

Hence we have sensitivity to Initial Conditions
Lyapunov Exponent
Largest Lyapunov
Exponent of 30
Mobile Stations
λ
Lyapunov Exponent
of 20 Mobile Stations
Lyapunov Exponent
of 10 Mobile Stations
Time (sec)



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.
Impact on QoS
10 Users
 Node-B 1

Node-B 2
Average Throughput (Kbits/sec)
Average Throughput (bits/sec)
400.000
300.000
100.000
0
Time (min)
Unfairness of
80
(kbits/sec)
Unfairness
of
30 Users
80 (kbits/sec)
200.000
0

Node-B 1

Node-B 2
5m
10m
Time (min)

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
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.
12
UE 10
UE 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.
10
Delay
8
6
4
2
0
0
100
200
300
400
Time (sec)
500
600
700
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)