PERFORMANCE EVALUATION OF 3G CORE NETWORK

NODES

Andrey Krendzel, Jarmo Harju

Tampere University of Technology (TUT), Institute of Communications Engineering, Tampere, Finland

Sergey Lopatin

St.-Petersburg Research and Development Institute of Telecommunications (LONIIS), St.-Petersburg, Russia

Keywords: the Third Generation wireless systems, Universal Mobile Telecommunication System, Internet Protocol

Multimedia Core Network Subsystem, Fractional Brownian Motion, self-similarity.

Abstract: Wireless network planning is a very complex process, the result of which influences on the success of

network operators. A poorly planned network cannot achieve the required Quality of Service. It also

involves extra costs and fewer benefits for its network operator. Actually, wireless network planning deals

with a large number of different aspects. In this paper Core Network (CN) planning aspects for the third

generation (3G) wireless systems are discussed. The problem of performance evaluation of 3G CN nodes

for Internet Protocol Multimedia Core Network Subsystem (IM CN subsystem) is considered in details

taking into account self-similarity caused by the high variability of burstiness of multiservice traffic in 3G

wireless networks. The method for the problem solution is based on the use of FBM/D/1/W queueing

system (FBM – Fractional Brownian Motion).

1 INTRODUCTION

There has been an evolution in wireless

communications almost every ten years. The first

generation (1G) in 1980s and the second generation

(2G) mobile systems in 1990s have been oriented

mainly for providing circuit-switched (CS) services

to users. The 2G subscribers have used the rate for

data transfer up to 14 kb/s as a maximum. In 1996,

European Telecommunications Standards Institute

(ETSI) decided to enhance 2G GSM standard in

annual Phase 2+ releases that incorporate the third

generation (3G) features such as General Packet

Radio Service (GPRS) and Enhanced Data Rates for

GSM Evolution (EDGE). The data rates for users of

the systems are limited to less than several hundreds

of kb/s.

Universal Mobile Telecommunications System

(UMTS) as the 3G mobile system will be introduced

during first decade of new century. It is specified by

ETSI and the world-wide 3G Partnership Project

(3GPP) within the framework defined by the

International Telecommunication Union (ITU) and

known as International Mobile Telecommunications

- 2000 (IMT-2000). The 3G systems can support 2

Mb/s for indoor environments and at least 144 kb/s

for vehicular environments.

ETSI and 3GPP are introducing UMTS in

phases and annual releases. UMTS Rel’3

(sometimes called as Rel’99) is a 3G GSM successor

standard using the GSM Phase 2+ enhanced core

network (CN). The most important evolutionary step

toward UMTS is to introduce a packet switched core

network (PS CN) domain. The main function of the

PS CN domain is to support all services (GPRS,

WAP, etc.) provided to both GSM subscribers and

UMTS users (Kaaranen H. , et. al 2001).

The following phases after Rel’3 specify how

voice and multimedia can be supported by IP

technology. It is characterized by creating of the

Internet Protocol (IP) Multimedia Core Network

Subsystem (IM-subsystem), which comprises all PS

CN domain elements for providing

telecommunication services within UMTS Rel’4,5,6.

The IM-subsystem contains a uniform way to

maintain Voice over IP (VoIP) calls and offers a

platform to multimedia services. The examples of

IM services are voice telephony, real-time

interactive games, videotelephony, instant

97

Krendzel A., Harju J. and Lopatin S. (2005).

PERFORMANCE EVALUATION OF 3G CORE NETWORK NODES.

In Proceedings of the Second International Conference on e-Business and Telecommunication Networks, pages 98-104

DOI: 10.5220/0001415600980104

Copyright

c

SciTePress

messaging, emergency calls, multimedia conferencing (Bale M.C. , 2001). In the UMTS

Rel’5,6 all traffic coming from Radio Access

Network (RAN) to the CN is supposed to be all IP

based (Kaaranen H. , et. al 2001).

The next step of wireless communications

evolution is the fourth generation (4G) of mobile

communication systems (the systems beyond IMT-

2000). Now it is difficult to predict when the 3G

evolution ends and the 4G really starts (Kaaranen

H. , et. al 2001). The 4G systems should offer

significantly higher bit rate than 2 Mb/s, have high

capacity with a low bit cost and be able to support

all type of telecommunication services from the

viewpoint of multimedia communications

(Y.Yamao et. al, 2000). It is supposed that on the

CN side of the 4G systems the main purpose is to

minimize changes and utilize the 3G CN elements

and the 3G CN functionality as much as possible

(Kaaranen H. , et. al 2001). The CN development is

summarized in the Table I.

There are some important features of the global

evolution process in wireless communications.

The 3G wireless systems should be designed to

support for a high-speed transfer of a large amount

of multimedia information between users. One of

the main properties of the data traffic in the 3G

systems is a large diversity depending on the profile

of services provided to 3G users. It is expected that

the traffic in the 3G systems will expand

considerably (The UMTS 3G Market Forecasts,

2002).

Table 1: Core Network development

GENERATIONS OF

WIRELESS

SYSTEMS

CORE NETWORK

DOMAINS

2G CS CN

2G phase 2 + CS CN and PS CN

3G (UMTS Rel’3) CS CN and PS CN

(enhanced 2G phase 2 +

CN)

3G (UMTS Rel’ 4) CS CN, PS CN, IM CN

3G (UMTS Rel’ 5,6) IM CN

4G IM CN

(enhanced 3G CN)

The growing data/multimedia traffic leads to

increasing the total load on network subsystem

elements. Moreover, traffic patterns generated by

3G services may be quite different from traditional

Poisson models used for circuit switched voice

traffic. When modeling packet-switched

multiservice networks it is necessary to take into

account the notion of self-similarity (M. Jiang et al.

2001), (V. Paxson, S.Floyd, 1994). Due to the high

variability of burstiness of the traffic, the use of the

classical teletraffic theory for a performance

evaluation of PS CN domain elements may give

essential faults; in particular, the network

parameters can be underestimated. Such faults are

unacceptable when IM-subsystem planning as well,

therefore, principles of the teletraffic theory cannot

be applied in this case.

Due to above reasons, the following 3G

network planning problems occur:

• the prediction problem of a demand for 3G

services;

• the estimation problem of 3G data traffic

parameters;

• the problem of the performance evaluation

of IM-subsystem nodes taking into account the

self-similar nature of the multiservice traffic.

It is seen from the Table 1 that the CN evolution

is quite temperate. From the viewpoint of functional

capabilities the enhanced CN of the 3G systems will

be able to support 4G services (Kaaranen H. , et. al

2001). So, it is expected that the 4G RAN will

undergo the main changes, from the viewpoint of

CN only resource scaling is required. For these

reason it is very important to develop solution

methods for the above-mentioned CN planning

problems. It will enable planning 3G/4G networks

in such a way that both technical and economical

advantages can be achieved when constructing and

exploiting the networks.

In this paper one of the main problems of Core

Network planning is considered in details. This is

the problem of performance evaluation of IM-

subsystem elements. This problem arises because of

the fact that the traffic generated by 3G services

may be self-similar or long-range dependent in

nature (i.e., bursty over a wide range of time

scales).

Self-similarity is observed in different

networks; in particular, in local area networks

(Willinger W. et. al, 1995), Internet (Roberts J.B.,

1998), wireless networks (M. Jiang et. al, 2001) and

others. It is shown in (R. Kalden, S. Ibrahim, 2004)

that in GPRS in the case of aggregated traffic and

also in the case of individual WAP and WEB traffic

traces, the results strongly suggest long-range

dependency (values of the Hurst parameter are

about 0.8). Besides, the packet arrival process of

WAP and WEB traffic may be considered as a class

of processes consisting of the superposition of an

infinite number of ON/OFF-sources. Through the

characterization of the sum of the covariances, it is

possible to establish a simple explicit necessary and

sufficient condition for the process to be long-range

dependent (F. Geerts, C. Blondia, 1998). It is

reasonable to suppose that self-similarity may occur

ICETE 2005 - WIRELESS COMMUNICATION SYSTEMS AND NETWORKS

98

in 3G wireless networks as well. This is in sharp

contrast to commonly made traffic modeling

assumptions, because self-similarity is

characterized by stronger dependence of a variance

from time than linear dependence (R. Kalden, S.

Ibrahim, 2004). The traffic does not smooth out in

the case of aggregation, leading to congestion

situations and packet-drops due to the burstiness of

the traffic. In the case of self-similar traffic the

applied methods for performance analysis and

network dimensioning are different from those

applied to statistically more simple traffic, which

can be modeled with Markovian processes (W.

Willinger et. al, 1997), (A. Adas, 1997). For

example, the queue tail behavior is heavy-tailed in

the case of self-similar input traffic (Norros I,

1994).

Thus, the use of the classic teletraffic theory for

a performance evaluation of packet multiservice

network elements gives essential faults, in

particular, network parameters may be

underestimated (Roberts J.B., 2001), (W. Willinger

et. al, 1994), (W. Stallings,1998). In literature

(Norros I., 1994), (Norros I., 1995), (Addie R.G. et

al.,1998) the approaches of overcoming such sort of

difficulties are considered. In our research the

results from (Norros I, 1995) concerning self-

similar multiservice traffic is developed and applied

for evaluation of probabilistic and time

characteristics of such IM-subsystem element as

Gateway GPRS Support Node (GGSN) for the

UMTS Rel’5 IM-subsystem. The method for GGSN

performance evaluation is based on using the

FBM/D/1/W queueing system.

2 UMTS Rel’5 CORE

NETWORK

ARCHITECTURE ASPECTS

The reference architecture for UMTS Rel’ 4 and

Rel’ 5 from 3GPP TR 23.821 is the same (Kaaranen

H. , et. al 2001). In the development of UMTS Rel’

5 the focus has shifted to the PC CN domain, which

has been extended with IM-subsystem

functionality. The vision of UMTS Rel’5 from the

All IP point of view taken from (Kaaranen H. , et.

al 2001) is shown in Fig. 1. As seen from Fig. 1, the

principle of allocation of data flows between end

users and GGSN leads to increasing of the load on

the network elements while approaching to GGSN.

The GGSN is the node that the most exposed to the

self-similarity influence in UMTS. The most

important events determining the load on GGSN on

the network level are arriving IP packets. Currently,

a transport technology for delivery of IP packets

to/from GGSN is not defined uniquely. For

instance, ATM may be applied as one of the

possible cases of such technology (W. Stallings,

1998).

Figure 1: Vision of UMTS Rel’ 5 (all IP)

3 THE LOAD MODEL

It is assumed that values s(t) of a random process

with interdependent increments are the total load

arrived to the node (server) in the time point t>0.

Current values s(t) in the time interval [0, t) may be

determined by the number of information units

(bytes, ATM cells, IP packets, and so on. If the

corresponding process is ordinary then an

increment is one information unit. The increments

intensity is the rate parameter

λ

, 1/s. Realizations of

process s(t) are non-decreasing step functions with

increments taking place in random time points.

Let us consider a random variable

(

)

[

)

tTTtsS

T

,0, ⊂

=

=

that is a sample of a random

process s(t). By definition

T

S is a sum of

interdependent identically distributed random

variables. If

0)( >>

=

TSE

T

λ

then conditions of

the central limiting theorem are fulfilled. Here,

(

)

⋅

E

is operator of statistical averaging. Accordingly,

T

S may be approximated by a Gaussian random

variable (Kleinrock L., 1976). Taking into account

abovementioned assumptions

T

S may be defined

as:

xTbTS

T

⋅+= )(

λ

,

(1)

where x = N(0,1) is a normalized Gaussian random

variable with the zero mean and the unit variance,

b(T) is a variance of

T

S .

If

ttb

2

)(

σ

=

and t > 0 then the univariate

probabilities distribution of the process s(t)

coincides with the corresponding distribution of a

Brownian motion process or a displaced Wiener

process (Karatsas I. et. al, 1997), (Papoulis A.,

1984). Similarly, the process s(t) corresponds to a

MS

BTS BSC

ISDN

PSTN

CSPDN

U

m

Network Management (NMS)

GERAN

V

A

S

IN

CAMEL

WAP

CN PS Domain

SGSN GGSN

IP Multimedia

UE

BS RNC

Uu

UTRAN

HSS

IMCN

IP/ATM

IP/ATM

IP/ATM

I

u

MS

BTS BSC

ISDN

PSTN

CSPDN

U

m

Network Management (NMS)

GERAN

V

A

S

IN

CAMEL

WAP

CN PS Domain

SGSN GGSN

IP Multimedia

UE

BS RNC

Uu

UTRAN

HSS

IMCN

IP/ATM

IP/ATM

IP/ATM

I

u

PERFORMANCE EVALUATION OF 3G CORE NETWORK NODES

99

Poisson process if condition (1) is fulfilled when x

is a Poisson random variable and

ttb

λ

=)( .

It is necessary to take into account a self-

similarity notion when load modeling in packet data

networks. There are different ways of self-similarity

load modeling (W. Willinger et. al, 1997), (Norros

I. et. al, 1995), (Addie R.G. et. al, 1998). With

reference to (1) self-similarity may be taken into

account as

,15.0,)()(

22

<≤= HTTb

H

σ

(2)

where H is the Hurst parameter. Expressions (1)

and (2) specify a model of a total traffic load

arriving to a server input by a time point t=T.

4 THE QUEUEING SYSTEM

MODEL

It is assumed that s(t) arrives to the server input.

The server is modeled by queueing system with

deterministic rate C, 1/sec and the buffer size (W-1),

∞<≤ W1 . The queueing system is the stable one

because there is a stationary probability distribution

if C >

λ

. In accordance with the Kendall’s notation

for queues the system is G/D/1/W (Kleinrock L.,

1975). The corresponding system may be also

defined as FBM/D/1/W (Norros I, 1995) if the

expressions (1,2) are fulfilled. Here, the FBM is a

normalized fractional Brownian motion, i.e. the

corresponding process is a strictly self-similar one.

5 THE TASK ESTIMATION

DEFINITION

When stability conditions are fulfilled the average

value of the total load arrived to the queueing input

by the time point t=T>0 is less than the queueing

system can serve for the same time interval. It

should be emphasized that the load is a random

process. Therefore, it is possible to appear an event

when the buffer will be overflow. The probability

of the event is defined by statistical properties of an

unserved traffic process that may be written as

[]

CttStV −= )(,0max)(

(3)

The introduction of operator max [0, x] in (3) is

caused by nonnegative values of an unserved load.

It is similar to the introduction of an adsorbing

barrier in the coordinate origin point for the

displaced self-similar (fractional) Wiener process.

The estimation problem is to determine values of

parameters C and W. It should be done by taking

into account the following condition. The

probability that the unserved load will be greater

than the parameter W must not exceed the preset

threshold

ε

:

[

]

10,)( <<<>

=

>

ε

ε

ε

tWtVP

(4)

6 THE TASK ESTIMATION

DEFINITION

Taking into consideration the approximation of the

random process s(t) sample by the random Gaussian

variable defined by the expressions (1,2) we have

the lower bound for the buffer saturation

probability

[

]

[

]

)(max)(

0

TxPWtVP

T

α

>≥>

>

,

(5)

where

(

)

[

]

H

TWTCT )()(

2

σλα

+−= .

The expression (5) shows that the probability of

events union is not less than the probability of each

event. Taking into account that the random variable

X is the normalized displaced Gaussian random

one, the expression (5) may be transformed as

[]

[]

)2/)(exp(5.0max

2/)2/exp(max)(

2

0

2

)(

0

T

dxxWtVP

T

T

T

α

π

α

−≈

⎥

⎥

⎦

⎤

⎢

⎢

⎣

⎡

−≥>

>

∞

>

∫

(6)

Let us take into consideration the logarithmic

function monotony for the expression that is

equivalent (4,6). Then the expression binding the

parameters C, W, λ and the buffer saturation

probability

ε

is

(

)

(

)

()

H

T

T

WTC

2

2

2

0

minln

σ

λ

ε

+−

≈−

>

(7)

The solution of the equation (7) may be found

by the parameter T differentiation and equating of

the obtained derivative with zero (Norros I, 1995).

It gives the following expression

()()

()()

()

H

T

m

T

WTC

CH

WH

T

2

2

2

0

minarg

1

σ

λ

λ

+−

=

−−

=

>

(8)

Substituting T

m

in (7) and transforming the

expression we finally get

,15.0,

)1(

ln2

1/

/1

1

1

<≤

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

−

−

+=

−

−

H

H

HW

nC

H

H

HH

ε

λ

(9)

where

λσ

/

2

=n .

Substituting n, W, λ and

ε

values in (9) we get

the upper bound (if H=0.8) and lower bound (if

ICETE 2005 - WIRELESS COMMUNICATION SYSTEMS AND NETWORKS

100

H=0.5) of the server service rate C. One of the main

parameters of queueing system is the inverse

parameter (9)

ρ = λ/

C called the utilization factor.

Fig. 2 illustrates dependences of the utilization

factor from the magnitude

λσ

/

2

=n

for various

values of the Hurst parameter and the buffer

capacity W when

ε

= 10

-5

.

Trends of the curves (Fig.2) show that it is very

important to take into account the self-similarity

influence while assigning server parameters. It

should be emphasized that the area of the

dependences when n=1 and H=0.5 (as shown in

Fig.2) corresponds to the case of the Poisson arrival

process.

0.998

0.198

p1 n()

p2 n()

x1 n()

x2 n()

z1 n()

z2 n()

4

0.5 n

0.5 1 1.5 2 2.5 3 3.5 4

0

0.2

0.4

0.6

0.8

H=0.50 W=1500

H=0.50 W=200

H=0.65 W=1500

H=0.65 W=200

H=0.80 W=1500

H=0.80 W=20

0

Utilization factor

Figure 2: Self-similarity influence on the server

utilization factor

It is wise to take the value

ε

sufficiently small.

It enables to have the acceptable probability of

messages blocking arriving on the server. In this

case the server buffer will be filled partly.

For determination of the upper bound of the

average queue length in the server buffer the

expression based on results (Norros I., 1994),

(Norros I. et. al, 1995) may by used:

()

(

)

()

()

HH

H

C

C

q

−

−

−

=

1

121

max

1

/

λ

λ

(10)

The classical result for M/D/1 system may be

applied for determination of the lower bound of the

average queue length:

()

)1(2/1

2

min

C

C

C

C

q

λ

λ

λ

λ

−

−

−

=

(11)

The upper and the lower bounds for average

service time (

τ

) are determined using Little result

(Kleinrock L., 1976):

CqCq /,/

minminmaxmax

=

=

τ

τ

,

(12)

Thus, using the expressions (9-12) it is possible

to determine bounds for the probabilistic and time

characteristics of the single server under the self-

similarity load influence.

7 CASE STUDY

In this section the example illustrating the above-

presented method is considered. Since there are no

exact regulations on transport network protocols on

Serving GPRS Support Node (SGSN)-GGSN

interface at the present moment it is assumed that

ATM is used as underlying technology for delivery

of IP packets. The rate of information units (ATM

cells) arriving on SGSN is multiple (k) of 2

Mbit/sec. The parameters characterizing the server

normal functionality may be estimated by the

following way.

Let k = 20 and in average 30% of the channel

throughput is in use during the messages delivery to

SGSN. Then, the value of the intensity of ATM

cells arriving on the SGSN input is

λ

≈ 30000 s

-1

. If

a number of SGSNs connected to the GGSN is 4

then the total value of the intensity of ATM cells

arriving to GGSN input is

λ

≈ 120000 s

-1

. In

accordance with (4.8), (4.9) and (4.10) the

relationships between the GGSN server capacity,

the upper bound for average queue length in the

GGSN buffer, the upper bound for the average

service time of information units in the GGSN

buffer and the parameter n

)(

2

λσ

=n

are shown

in Figures 3, 4, 5 respectively (W = 50, 200; H =

0.8;

ε

= 10

-7

).

8 CONCLUSION

In this paper the influence of self-similar input on

GGSN performance in UMTS Rel’5 IM-subsystem

has been analyzed. FBM/D/1/W queueing system

for evaluation of the GGSN parameters was

applied. The submitted method enables determining

the following probabilistic and time characteristics:

•

upper and lower bounds for the GGSN

service rate;

•

upper and lower bounds for the average

queue length in the GGSN buffer;

•

upper and lower bounds for the average

service time of information units in the

GGSN buffer;

PERFORMANCE EVALUATION OF 3G CORE NETWORK NODES

101

• the server utilization.

The obtained results point to a need to take into

account self-similarity while assigning the GGSN

parameters.

As well known, when providing multimedia

services based on IP technologies one of the main

aspects is to ensure Quality of Service (QoS). From

this point of view the presented method may be

extended for performance evaluation of other IM-

subsystem elements, in particular, for on Serving

GPRS Support Node (SGSN) performance

evaluation.

0246810

1

.

10

5

1

.

10

6

1

.

10

7

W=50 H=0.8

W=200 H=0.8

n

C

Figure 3: GGSN server capacity estimating

0246 810

1

.

10

4

1

.

10

3

0.01

0.1

1

10

100

W=50 H=0.8

W=200 H=0.8

n

q (max)

Figure 4: The upper bound for average queue length in

the GGSN buffer

0246 810

1

.

10

7

1

.

10

6

1

.

10

5

1

.

10

4

1

.

10

3

0.01

0.1

W=50 H=0.8

W=200 H=0.8

n

T (max), ms

Figure 5: The upper bound for the average service time in

the GGSN buffer

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