A

Unified

Approach

For

Improving

Qos

And

Provider

Revenue

In

Mobile

Networks

In

this

paper

we

introduce

a

unified

approach

for

the

adaptive

control

of

mobile

networks

in

order

to

improve

both

quality

of

service

-LRB-

QoS

-RRB-

for

mobile

subscribers

and

to

increase

revenue

for

service

providers

The

introduced

approach

constantly

monitors

QoS

measures

as

packet

loss

probability

and

the

current

number

of

active

mobile

users

during

operation

of

the

network

Based

on

the

values

of

the

QoS

measures

just

observed

the

system

parameters

of

the

admission

controller

and

packet

scheduler

are

controlled

by

the

adaptive

performance

management

entity

Considering

UMTS

we

present

performance

curves

showing

that

handover

failure

probability

is

improved

by

more

than

one

order

of

magnitude

Moreover

the

packet

loss

probability

can

be

effectively

regulated

to

a

predefined

level

and

provider

revenue

is

significantly

increased

for

all

pricing

policies

The

third

generation

-LRB-

-RRB-

of

mobile

networks

is

expected

to

complete

the

worldwide

globalization

process

of

mobile

communication

Since

different

parts

of

the

worlds

emphasize

different

issues

the

global

term

has

regional

synonyms

In

the

US

and

Japan

often

carries

the

name

International

Mobile

Telephony

-LRB-

-RRB-

In

Europe

has

become

Universal

Mobile

Telecommunications

System

-LRB-

UMTS

-RRB-

following

the

ETSI

perspective

The

European

industrial

players

have

created

the

Generation

Partnership

Project

-LRB-

-RRB-

-LSB-

-RSB-

for

the

standardization

of

UMTS

mobile

networks

provide

the

foundation

for

new

services

with

high-rate

data

not

provided

by

current

second

generation

systems

-LSB-

-RSB-

While

the

standardization

of

is

still

ongoing

the

discussion

of

technical

issues

beyond

has

already

started

-LSB-

-RSB-

Recently

Aretz

et

al.

reported

a

vision

for

the

future

of

wireless

communication

systems

beyond

that

consists

of

a

combination

of

several

optimized

access

systems

on

a

common

IP-based

medium

access

and

core

network

platform

-LSB-

-RSB-

Charging

and

pricing

are

essential

issues

for

network

operations

of

mobile

networks

A

primary

target

of

differen-tiated

pricing

of

Internet

services

is

the

prevention

of

system

overload

and

an

optimal

resource

usage

according

to

different

daytimes

and

different

traffic

intensities

-LSB-

-RSB-

Among

the

proposed

pricing

proposals

flat-rate

pricing

-LSB-

-RSB-

is

the

most

common

mode

of

payment

today

for

bandwidth

services

Flat-rate

pricing

is

popular

because

of

its

minimal

accounting

overhead

A

flat-rate

encourages

usage

but

does

not

offer

any

motivation

for

users

to

adjust

their

demand

Dynamic

pricing

models

that

take

the

state

of

the

network

into

account

in

the

price

determination

have

been

proposed

as

being

more

Corresponding

author

responsive

Usage-based

pricing

regulates

usage

by

imposing

a

fee

based

on

the

amount

of

data

actually

sent

whereas

congestion-sensitive

pricing

uses

a

fee

based

on

the

current

state

of

congestion

in

the

network

Thus

a

unified

approach

considering

both

dynamic

pricing

and

controlling

quality

of

service

-LRB-

i.e.

performance

management

-RRB-

provides

an

effective

tool

for

the

operation

of

mobile

networks

However

in

previous

work

-LSB-

-RSB-

the

improvement

of

Quality

of

Service

-LRB-

QoS

-RRB-

in

mobile

networks

and

the

optimization

of

mobile

service

provider

revenue

has

been

considered

sepa-rately

The

Quality

of

Service

-LRB-

QoS

-RRB-

concept

and

architecture

for

UMTS

networks

specified

in

-LSB-

-RSB-

provides

means

for

sharing

radio

resources

among

different

groups

of

users

according

to

their

individual

QoS

demands

Furthermore

the

concept

of

UMTS

management

and

control

functions

such

as

admission

controller

and

resource

manager

is

roughly

outlined

Das

et

al.

proposed

a

framework

for

QoS

provisioning

for

multimedia

services

in

wireless

access

networks

-LSB-

-RSB-

They

developed

an

integrated

framework

by

combining

various

approaches

for

call

admission

control

channel

reservation

bandwidth

degradation

and

bandwidth

compaction

In

-LSB-

-RSB-

we

introduced

a

framework

for

the

adaptive

control

of

UMTS

networks

which

utilizes

online

monitoring

of

QoS

measures

-LRB-

e.g.

handover

failure

and

call

blocking

probabilities

-RRB-

in

order

to

adjust

system

parameters

of

the

admission

controller

and

the

packet

scheduler

The

presented

approach

is

based

on

a

lookup

table

called

the

Performance

Management

Information

Base

-LRB-

P-MIB

-RRB-

Entries

of

the

P-MIB

have

to

be

determined

using

extensive

off-line

simulation

experiments

to

determine

optimal

parameter

configuration

for

the

considered

scenarios

Given

the

entries

of

the

P-MIB

we

showed

how

to

improve

QoS

for

mobile

users

by

periodi-cally

adjusting

system

parameters

The

practical

applicability

of

this

approach

is

limited

if

the

P-MIB

comprises

many

C.

LINDEMANN

ET

AL.

tries

-LRB-

i.e.

many

scenarios

have

to

be

considered

-RRB-

because

of

the

high

computational

effort

for

determining

these

entries

by

simulation

This

paper

introduces

a

unified

approach

for

the

adaptive

performance

management

for

mobile

networks

As

the

main

result

of

the

paper

the

introduced

approach

is

based

on

a

mathematical

framework

for

the

proposed

update

schemes

rather

than

a

lookup

table

As

a

consequence

the

adaptive

control

mechanism

can

be

adjusted

in

an

intuitive

way

and

optimal

system

parameter

configuration

can

efficiently

be

determined

We

effectively

utilize

adaptive

performance

management

for

improving

not

only

QoS

for

mobile

users

but

also

increase

revenue

earned

by

service

providers

As

in

-LSB-

-RSB-

controlled

system

parameters

comprise

queueing

weights

for

packet

scheduling

a

threshold

value

of

the

access

queue

for

admission

of

non

real-time

traffic

and

a

portion

of

the

overall

available

bandwidth

reserved

for

handover

calls

Beyond

-LSB-

-RSB-

we

propose

a

scheme

for

adjusting

the

queueing

weights

for

both

improving

QoS

for

higher

priority

users

that

suffer

from

a

high

population

of

users

with

lower

priority

and

for

increasing

the

revenue

earned

by

the

service

provider

For

the

analysis

of

the

update

strategy

of

the

queuing

weights

we

consider

a

usage-based

and

a

usage

throughput-based

pricing

policy

according

to

-LSB-

-RSB-

Furthermore

we

introduce

a

hybrid

pricing

policy

combining

the

notion

of

flat-rate

and

a

usage-based

pricing

according

to

current

policies

of

GSM

networks

Performance

curves

derived

by

simulation

evidently

illustrate

the

gain

of

the

unified

approach

for

adaptive

performance

management

In

fact

for

UMTS

networks

simulation

results

show

that

handover

failure

probability

can

be

improved

by

more

than

one

order

of

magnitude

Moreover

packet

loss

probability

can

be

effectively

regulated

to

a

predefined

level

and

the

provider

revenue

is

significantly

increased

for

all

considered

pricing

policies

The

paper

is

organized

as

follows

Section

introduces

the

unified

approach

for

adaptive

performance

management

and

describes

its

embedding

in

the

system

architecture

of

mobile

networks

Section

introduces

strategies

for

controlling

the

parameters

of

an

admission

controller

in

order

to

improve

QoS

Section

describes

the

parameter

control

of

a

packet

scheduler

for

the

combined

improvement

of

both

QoS

and

provider

revenue

In

section

we

present

simulation

results

that

illustrate

the

benefit

of

employing

the

proposed

approach

for

adaptive

performance

management

Finally

concluding

remarks

are

given

This

section

introduces

the

unified

approach

for

regularly

adjusting

system

parameters

to

changing

traffic

load

packet

arrival

pattern

or

population

of

users

etc.

We

consider

a

cellular

mobile

network

in

which

a

different

transceiver

station

serves

each

cell

The

purpose

of

the

transceiver

station

is

the

modu-Figure

System

architecture

for

adaptive

performance

management

lation

of

carrier

frequencies

and

demodulation

of

signals

Furthermore

a

base

station

controller

-LRB-

BSC

-RRB-

is

considered

that

is

responsible

for

a

cluster

of

cells

i.e.

several

transceiver

stations

The

BSC

manages

the

radio

resources

i.e.

schedules

data

packets

and

controls

handovers

inside

the

cell

cluster

as

well

as

handovers

towards

and

from

neighboring

cell

clusters

To

improve

QoS

for

mobile

users

as

well

as

to

increase

revenue

earned

by

service

providers

an

entity

for

Adaptive

Performance

Management

-LRB-

APM

-RRB-

is

included

in

a

BSC

Furthermore

a

BSC

has

to

be

extended

by

an

online

performance

monitoring

component

that

derives

QoS

measures

in

a

certain

time

window

-LRB-

e.g.

handover

failure

probabilities

of

mobile

users

or

packet

loss

probabilities

-RRB-

These

QoS

measures

form

a

system

pattern

that

is

submitted

in

fixed

time

intervals

-LRB-

i.e.

a

control

period

-RRB-

to

the

APM

entity

which

subsequently

updates

corresponding

system

parameters

-LRB-

i.e.

parameters

of

traffic

controlling

components

like

the

admission

controller

and

packet

scheduler

-RRB-

Thus

the

proposed

approach

closes

the

loop

between

network

operation

and

network

control

Figure

shows

the

system

architecture

for

performance

management

embedded

in

a

BSC

Online

performance

monitoring

System

parameters

of

a

BSC

can

be

effectively

updated

by

monitoring

QoS

measures

which

are

immediately

affected

by

these

parameters

A

current

value

for

a

QoS

measure

is

determined

online

based

on

a

set

of

relevant

events

corresponding

to

this

QoS

measure

-LRB-

e.g.

packet

arrivals

are

relevant

events

for

computing

packet

loss

probabilities

-RRB-

The

online

monitoring

of

QoS

measures

is

done

by

a

sliding

window

technique

as

introduced

in

-LSB-

-RSB-

The

width

of

the

sliding

window

over

time

depends

on

the

number

of

relevant

events

that

are

occurred

according

to

a

QoS

measure

Upon

arrival

of

a

new

relevant

event

the

sliding

window

moves

in

time

At

the

end

of

a

control

period

the

QoS

measures

are

derived

for

each

sliding

window

-LRB-

e.g.

packet

loss

probability

can

be

derived

from

number

of

lost

packets

divided

by

number

of

all

packet

arrivals

in

the

sliding

window

-RRB-

These

QoS

measures

and

the

number

of

events

occurred

in

the

last

control

period

form

the

system

pattern

that

is

transferred

to

the

adaptive

performance

management

entity

-LRB-

see

figure

-RRB-

IMPROVING

QoS

AND

PROVIDER

REVENUE

IN

MOBILE

NETWORKS

Note

that

an

accurate

online

monitoring

of

QoS

measures

requires

a

specific

width

for

the

sliding

window

A

certain

number

of

events

representing

the

history

of

the

QoS

measure

have

to

be

considered

to

get

an

expressive

measure

On

the

other

hand

considering

a

big

sliding

window

prevents

the

APM

entity

from

fast

reaction

on

changing

traffic

conditions

A

bigger

sliding

window

contains

more

history

and

thus

more

events

have

to

be

collected

to

cause

a

significant

change

in

the

online

monitored

QoS

measure

This

tradeoff

between

accurate

online

monitoring

and

fast

reaction

of

the

APM

to

changing

traffic

conditions

has

to

be

studied

carefully

in

several

experiments

to

get

the

optimal

width

of

the

sliding

window

for

each

QoS

measure

Adaptive

performance

management

Whenever

a

system

pattern

S

-LCB-

-LRB-

P

n

-RRB-

-LRB-

P

m

n

m

-RRB-

-RCB-

consisting

of

online

monitored

QoS

measures

P

P

m

and

the

numbers

of

relevant

events

n

n

m

occurred

in

the

last

control

period

is

transmitted

to

the

APM

an

update

of

the

system

parameters

can

be

performed

In

general

an

update

of

a

system

parameter

is

made

according

to

a

function

f

depending

on

a

subset

of

the

QoS

measures

P

P

m

and

the

previous

value

-LRB-

old

-RRB-

of

the

system

parameter

Let

P

-LRB-

-RRB-

P

-LRB-

k

-RRB-

k

m

be

the

QoS

measures

corresponding

to

system

parameter

then

the

update

is

made

if

a

certain

minimum

number

n

-LRB-

-RRB-

of

relevant

events

occurred

in

the

last

control

period

That

is

-LRB-

new

-RRB-

f

P

-LRB-

-RRB-

P

-LRB-

k

-RRB-

-LRB-

old

-RRB-

if

min

-LCB-

n

-LRB-

-RRB-

n

-LRB-

k

-RRB-

-RCB-

n

-LRB-

-RRB-

-LRB-

-RRB-

We

classify

update

functions

in

relative

functions

that

perform

a

parameter

update

relative

to

the

old

parameter

value

and

absolute

functions

that

set

the

new

parameter

value

independent

of

the

old

value

i.e.

f

is

independent

of

-LRB-

old

-RRB-

in

-LRB-

-RRB-

With

relative

update

functions

strong

fluctuations

of

the

corresponding

system

parameter

in

one

update

step

can

be

avoided

In

section

we

study

a

special

class

of

relative

update

functions

in

order

to

set

the

parameters

of

an

admission

controller

Furthermore

we

develop

in

section

an

absolute

update

function

for

adjusting

the

weights

of

a

weighted

fair

queueing

packet

scheduler

Economics

and

pricing

policies

in

mobile

networks

There

are

multiple

requirements

which

should

be

fulfilled

for

any

viable

pricing

mechanism

in

multi-service

class

data

communication

networks

-LSB-

-RSB-

A

primary

target

of

differen-tiated

pricing

of

Internet

services

is

the

prevention

of

system

overload

and

an

optimal

resource

usage

according

to

different

daytimes

and

different

traffic

intensities

Furthermore

the

pricing

scheme

should

be

implemented

in

a

completely

de-centralized

manner

and

there

should

be

multiple

priorities

in

order

to

take

into

account

the

different

QoS

required

by

different

applications

and

users

In

general

pricing

policies

can

be

partitioned

into

usage-based

-LRB-

pay-as-you-go

-RRB-

pricing

flat-rate

-LRB-

all-you-can-eat

-RRB-

pricing

and

dynamic

pricing

In

usage-based

pricing

policies

a

user

is

charged

according

to

a

connection

time

or

traffic

volume

Whereas

connection

based

calls

-LRB-

e.g.

in

GSM

-RRB-

are

charged

by

connection

time

packet-switched

services

-LRB-

e.g.

in

UMTS

-RRB-

are

charging

the

transferred

data

volume

Dynamic

pricing

models

take

into

account

the

state

of

the

mobile

radio

network

for

determining

the

current

price

of

a

service

Congestion-sensitive

pricing

as

a

particular

dynamic

pricing

model

has

been

shown

to

be

more

responsive

MacKie-Mason

and

Varian

introduced

the

concept

of

congestion-sensitive

pricing

in

their

smart

market

scheme

-LSB-

-RSB-

Under

this

model

the

actual

price

for

each

packet

is

determined

based

on

the

current

state

of

network

congestion

In

-LSB-

-RSB-

Rao

and

Petersen

discussed

the

optimal

pricing

of

priority

services

Analogously

to

the

smart

market

approach

Gupta

et

al.

presented

a

pricing

scheme

that

uses

priorities

on

the

packet-level

-LSB-

-RSB-

They

proposed

to

differentiate

Internet

traffic

according

to

delay

and

loss

requirements

For

the

analysis

of

the

update

strategy

of

the

queuing

weights

we

consider

in

section

a

usage-based

and

a

usage

throughput-based

pricing

policy

according

to

-LSB-

-RSB-

Furthermore

we

introduce

a

hybrid

pricing

policy

combining

the

notion

of

flat-rate

and

a

usage-based

pricing

according

to

current

policies

of

GSM

networks

The

proposed

approach

distinguishes

three

different

types

of

services

circuit-switched

services

packet-switched

real-time

services

-LRB-

RT

-RRB-

and

packet-switched

non

real-time

services

-LRB-

NRT

-RRB-

Typically

circuit-switched

services

are

voice

calls

from

a

GSM

mobile

station

As

proposed

by

RT

services

belong

to

the

conversational

and

streaming

classes

and

NRT

services

fall

into

the

interactive

and

background

classes

-LSB-

-RSB-

The

bandwidth

available

in

a

cell

must

be

shared

by

calls

of

these

different

service

classes

and

the

different

service

requirements

have

to

be

met

Before

a

mobile

session

begins

the

user

needs

to

specify

its

traffic

characteristics

and

desired

performance

requirements

by

a

QoS

profile

Then

an

admission

controller

decides

to

accept

or

reject

the

users

request

based

on

the

QoS

profile

and

the

current

network

state

as

e.g.

given

by

queueing

length

The

purpose

of

the

admission

controller

is

to

guarantee

the

QoS

requirements

of

the

user

who

requested

admission

while

not

violating

the

QoS

profiles

of

already

admitted

users

The

call

admission

criteria

will

be

different

for

each

service

class

The

QoS

profile

for

RT

sessions

specifies

a

guaranteed

bandwidth

to

be

provided

for

the

application

in

order

to

meet

its

QoS

requirements

If

the

network

can

not

satisfy

the

desired

bandwidth

the

corresponding

admission

request

is

rejected

Data

packets

arriving

at

the

BSC

are

queued

until

they

are

scheduled

to

be

transmitted

over

the

radio

link

For

NRT

sessions

we

consider

an

admission

controller

taking

into

account

free

buffer

space

in

the

NRT

queue

-LSB-

-RSB-

In

order

to

prevent

buffer

overflow

once

a

call

is

admitted

the

current

queueing

length

is

set

against

certain

buffer

availability

threshold

C.

LINDEMANN

ET

AL.

of

the

capacity

denoted

by

The

admission

criteria

for

voice

and

RT

handovers

are

the

same

as

for

new

voice

calls

and

RT

sessions

except

that

additional

handover

bandwidth

can

be

utilized

The

analysis

of

several

admission

control

schemes

for

cellular

systems

presented

in

-LSB-

-RSB-

showed

that

the

simple

reservation

scheme

-LRB-

i.e.

reserving

bandwidth

for

handover

calls

-RRB-

performs

remarkably

well

For

simple

cellular

networks

the

optimal

amount

of

bandwidth

reserved

for

handover

calls

can

be

determined

by

analytical

models

-LSB-

-RSB-

In

the

model

presented

here

we

denote

with

b

h

the

portion

of

the

overall

bandwidth

that

is

exclusively

reserved

for

handover

calls

from

neighboring

cells

The

considered

admission

controller

does

not

prioritize

NRT

handovers

over

new

NRT

sessions

Further

details

of

the

admission

controller

are

given

in

-LSB-

-RSB-

Adjusting

the

admission

controller

for

QoS

improvement

In

this

section

we

show

how

to

utilize

equation

-LRB-

-RRB-

for

setting

the

parameters

and

b

h

of

the

admission

controller

in

order

to

reduce

packet

loss

probability

and

handover

failure

probability

For

updating

the

system

parameters

we

split

the

general

function

introduced

in

section

into

separate

functions

each

depending

only

on

one

QoS

measure

Let

P

P

k

be

the

QoS

measures

corresponding

to

a

system

parameter

Then

equation

-LRB-

-RRB-

can

be

simplified

to

-LRB-

new

-RRB-

f

-LRB-

P

-RRB-

f

k

-LRB-

P

k

-RRB-

k

-LRB-

old

-RRB-

L

-LRB-

new

-RRB-

R.

-LRB-

-RRB-

The

interpretation

of

-LRB-

-RRB-

is

the

following

Each

update

function

f

i

describes

the

influence

that

the

QoS

measure

P

i

should

have

on

the

system

parameter

Subsequently

the

overall

update

is

performed

by

computing

the

arithmetic

mean

of

the

functions

f

i

multiplied

with

the

old

value

of

the

system

parameter

Note

that

the

value

-LRB-

new

-RRB-

must

be

truncated

at

a

certain

lower

bound

L

and

an

upper

bound

R

in

order

to

guarantee

that

the

computation

of

-LRB-

new

-RRB-

results

in

a

valid

value

of

the

system

parameter

As

basic

update

function

we

consider

a

logarithmic

linear

function

of

the

form

f

i

-LRB-

P

i

-RRB-

m

i

log

P

i

b

i

-LRB-

-RRB-

The

reason

for

this

choice

is

that

we

want

to

consider

QoS

measures

like

loss

probabilities

and

failure/blocking

probabilities

which

are

in

the

range

of

to

Therefore

a

logarithmic

shape

is

more

suitable

In

previous

work

-LSB-

-RSB-

we

have

studied

update

schemes

of

system

parameters

of

an

admission

controller

and

a

packet

scheduler

based

on

a

lookup

table

In

order

to

determine

the

optimal

entries

of

this

lookup

table

extensive

off-line

simulation

experiments

have

been

conducted

Applying

regression

statistics

to

the

entries

of

this

lookup

table

shows

that

these

entries

are

well

represented

by

functions

with

logarithmic

shape

Thus

besides

the

motivation

of

the

update

functions

given

here

their

choice

is

to

a

large

extend

originated

from

regression

statistics

conducted

in

earlier

work

The

strength

of

the

influence

of

f

i

on

-LRB-

new

-RRB-

can

be

adjusted

with

the

gradient

m

i

The

parameter

b

i

can

be

determined

by

the

following

interpretation

suppose

the

desired

level

of

the

QoS

measure

P

i

is

i

-LRB-

e.g.

the

desired

packet

loss

probability

is

-RRB-

That

is

if

the

online

measured

value

of

P

i

is

i

the

system

parameter

should

not

be

changed

in

the

update

step

from

the

point

of

view

of

measure

P

i

Therefore

we

chose

f

i

-LRB-

i

-RRB-

and

from

this

relation

we

get

b

i

m

i

log

i

Inserting

in

equation

-LRB-

-RRB-

results

in

the

final

form

of

the

update

function

f

i

-LRB-

P

i

-RRB-

m

i

log

P

i

i

-LRB-

-RRB-

For

ease

of

notation

we

abbreviate

the

QoS

measures

handover

failure

probability

and

new

call/session

blocking

probability

corresponding

to

voice

calls

and

RT

sessions

by

HFP

and

CBP

respectively

The

probability

of

a

packet

loss

due

to

buffer

overflow

in

the

NRT

queue

is

abbreviated

by

PLP

The

update

strategy

according

to

equations

-LRB-

-RRB-

-LRB-

-RRB-

is

justified

by

its

intuitive

understanding

and

the

performance

results

presented

in

section

The

suitability

of

update

functions

other

than

-LRB-

-RRB-

-LRB-

-RRB-

is

subject

for

further

study

and

out

of

the

scope

of

this

paper

Update

of

non

real-time

queue

threshold

Recall

that

a

system

parameter

update

is

performed

each

time

a

system

pattern

arrives

at

the

APM

entity

and

the

minimum

number

of

relevant

events

corresponding

to

this

system

parameter

is

reached

Determining

the

update

for

the

system

parameter

i.e.

determining

-LRB-

new

-RRB-

is

performed

corresponding

to

the

old

value

-LRB-

old

-RRB-

and

the

actually

observed

QoS

measure

PLP

That

is

-LRB-

new

-RRB-

f

-LRB-

PLP

-RRB-

-LRB-

old

-RRB-

-LRB-

new

-RRB-

-LRB-

-RRB-

The

truncation

of

-LRB-

new

-RRB-

at

the

lower

bound

guaranties

that

the

value

does

not

accumulate

near

zero

for

long

periods

of

low

traffic

load

The

minimum

number

of

relevant

events

required

for

an

update

of

is

counted

in

data

volume

rather

than

in

packet

arrivals

-LRB-

in

the

experiments

this

number

is

MB

-RRB-

The

setting

of

the

gradient

m

of

the

corresponding

update

function

is

derived

from

a

couple

of

experiments

for

different

values

of

the

gradient

We

found

m

to

be

suitable

Choosing

a

suitable

value

for

the

gradient

is

a

similar

tradeoff

as

explained

for

the

sliding

window

size

A

large

gradient

results

in

a

fast

update

of

the

system

parameter

in

a

few

number

of

update

steps

but

also

introduces

higher

fluctuations

of

the

system

parameter

over

time

We

demonstrate

the

speed

of

the

parameter

adjustment

in

an

experiment

in

section

Furthermore

several

experiments

for

different

desired

loss

values

are

presented

Update

of

fraction

of

bandwidth

reserved

for

handover

The

update

for

the

system

parameter

b

h

i.e.

determining

b

-LRB-

new

-RRB-

h

is

performed

based

on

the

old

value

and

the

actually

observed

QoS

measures

HFP

and

CBP

That

is

b

-LRB-

new

-RRB-

h

f

-LRB-

HFP

-RRB-

f

-LRB-

CBP

-RRB-

b

-LRB-

old

-RRB-

h

b

-LRB-

new

-RRB-

h

R.

-LRB-

-RRB-

IMPROVING

QoS

AND

PROVIDER

REVENUE

IN

MOBILE

NETWORKS

The

value

b

-LRB-

new

-RRB-

h

is

truncated

at

a

lower

bound

of

and

a

certain

upper

bound

R

which

is

a

fraction

of

the

overall

bandwidth

available

-LRB-

in

the

experiments

we

fix

R

-RRB-

The

truncation

at

the

lower

bound

is

for

the

same

reason

as

explained

above

In

fact

for

computing

b

-LRB-

new

-RRB-

h

two

QoS

measures

corresponding

to

the

actually

observed

HFP

and

CBP

are

taken

into

account

A

high

HFP

should

increase

b

-LRB-

new

-RRB-

h

but

this

obviously

also

increases

the

CBP

because

less

bandwidth

is

available

for

new

voice

calls

and

RT

sessions

Therefore

the

HFP

and

the

CBP

influence

the

handover

bandwidth

b

-LRB-

new

-RRB-

h

In

fact

m

m

holds

in

the

update

functions

f

and

f

From

a

couple

of

experiments

for

different

gradients

we

found

m

to

be

suitable

A

common

assumption

in

cellular

networks

is

to

prioritize

handover

calls

over

new

calls

Therefore

the

desired

handover

failure

level

should

be

smaller

than

the

desired

call

blocking

level

According

to

these

values

the

handover

bandwidth

is

slightly

increased

if

HFP

is

equal

to

CBP

With

the

presented

strategy

the

parameters

of

the

update

functions

can

be

chosen

in

an

intuitive

way

and

optimal

parameter

configuration

can

efficiently

be

determined

This

is

the

major

advantage

over

the

approach

based

on

a

Performance

Management

Information

Base

introduced

in

-LSB-

-RSB-

which

requires

extensive

off-line

simulation

experiments

At

a

BSC

responsible

for

a

cluster

of

cells

data

packets

from

various

connections

arrive

and

are

queued

until

bandwidth

for

transmission

is

available

In

order

to

distinguish

different

priorities

for

NRT

traffic

corresponding

to

the

traffic

handling

priority

defined

by

-LSB-

-RSB-

scheduling

algorithms

like

Weighted

Round

Robin

-LRB-

WRR

-RRB-

Weighted

Fair

Queueing

-LRB-

WFQ

-LSB-

-RSB-

-RRB-

or

Class

Based

Queueing

-LRB-

CBQ

-LSB-

-RSB-

-RRB-

have

to

be

implemented

An

overview

of

queueing

issues

for

guaranteed

performance

services

can

be

found

in

-LSB-

-RSB-

In

WFQ

the

weights

control

the

amount

of

traffic

a

source

may

deliver

relative

to

other

active

sources

during

some

period

of

time

From

the

scheduling

algorithm

's

point

of

view

a

source

is

considered

to

be

active

if

it

has

data

queued

in

the

NRT

queue

Let

B

be

the

overall

bandwidth

available

for

NRT

sessions

at

time

t.

For

an

active

source

i

with

weight

w

i

the

bandwidth

B

i

that

is

allocated

to

this

transfer

at

time

t

is

given

by

B

i

w

i

j

w

j

B.

-LRB-

-RRB-

In

-LRB-

-RRB-

the

sum

is

taken

over

all

active

NRT

sources

j

A

class

based

version

of

WFQ

serves

packets

of

each

priority

class

according

to

the

weights

rather

than

every

active

source

Adjusting

the

packet

scheduler

for

QoS

and

revenue

improvement

This

section

utilizes

the

proposed

approach

for

the

adaptive

control

of

the

weights

of

a

weighted

fair

queueing

packet

scheduler

in

order

to

improve

QoS

as

well

as

to

increase

the

revenue

The

strategy

for

adjusting

the

weights

combined

with

the

introduction

of

several

pricing

policies

constitutes

a

further

contribution

of

the

paper

Recall

that

the

revenue

earned

by

a

mobile

service

provider

is

determined

by

the

monthly

payment

of

mobile

users

as

well

as

by

the

additional

usage-based

pricing

after

the

monthly

amount

of

data

volume

is

consumed

Note

that

the

monthly

subscription

rate

is

only

relevant

for

monthly

revenue

calculations

In

this

section

we

consider

the

revenue

improvement

in

a

certain

small

time

period

regardless

the

monthly

subscription

rates

In

section

we

briefly

discuss

monthly

revenue

calculation

Let

P

denote

the

number

of

different

priority

classes

i.e.

weights

of

the

weighted

fair

queueing

scheduler

Define

by

b

i

-LRB-

t

-RRB-

the

transferred

data

volume

in

time

t

of

users

of

priority

i

and

by

r

i

-LRB-

t

-RRB-

the

payment

of

users

of

priority

i

at

time

t

i.e.

the

user

pays

for

the

transferred

data

volume

We

distinguish

a

pure

usage-based

and

a

usage

throughput-based

pricing

policy

-LRB-

a

-RRB-

A

user

of

priority

i

has

a

fixed

payment

p

i

per

kbit

during

his

session

i.e.

r

i

-LRB-

t

-RRB-

p

i

-LRB-

b

-RRB-

The

payment

of

a

user

of

priority

i

consists

of

a

fixed

part

p

i

that

is

increased

proportional

to

the

additional

throughput

i

-LRB-

t

-RRB-

he

received

due

to

the

update

of

the

queueing

weights

i.e.

r

i

-LRB-

t

-RRB-

p

i

i

-LRB-

t

-RRB-

According

to

the

proposed

data

volume

based

pricing

with

respect

to

different

priority

classes

the

revenue

function

-LRB-

t

-RRB-

is

given

by

-LRB-

t

-RRB-

P

i

r

i

-LRB-

t

-RRB-

b

i

-LRB-

t

-RRB-

-LRB-

-RRB-

The

revenue

function

of

equation

-LRB-

-RRB-

is

utilized

in

section

for

evaluating

the

strategies

for

revenue

improvement

presented

below

Update

of

WFQ

weights

Recall

that

packets

of

NRT

users

arriving

at

the

BSC

are

first

queued

until

they

are

scheduled

for

transfer

by

a

weighted

fair

queueing

discipline

Let

w

i

w

i

i

P

be

the

basic

weights

of

the

WFQ

scheduler

The

update

of

the

queueing

weights

i.e.

determining

w

-LRB-

new

-RRB-

i

is

made

according

to

an

absolute

update

function

depending

on

the

basic

weights

w

i

and

the

current

number

of

NRT

sessions

belonging

to

priority

i.

Therefore

every

system

pattern

that

is

transmitted

from

the

online

monitoring

component

to

the

adaptive

performance

management

entity

contains

the

current

number

of

active

NRT

sessions

with

priority

i

in

the

cell

For

ease

of

notation

the

number

of

active

non

real-time

sessions

with

priority

i

is

abbreviated

by

NRT

i

The

idea

behind

the

strategy

for

revenue

improvement

is

to

shift

the

overall

utilization

of

bandwidth

for

NRT

traffic

C.

LINDEMANN

ET

AL.

towards

higher

priority

users

which

pay

more

for

the

transferred

data

volume

Note

that

the

update

strategy

should

be

conservative

in

a

way

that

the

transfer

of

packets

of

low

priority

is

not

simply

blocked

if

packets

of

higher

priorities

are

arriving

i.e.

priority

queueing

Assuming

that

the

majority

of

users

will

buy

a

cheaper

low

priority

service

class

priority

queueing

will

leave

most

users

unsatisfied

Therefore

the

update

strategy

also

considers

the

QoS

aspect

The

update

strategy

concerning

the

queueing

weights

is

developed

according

to

the

following

premises

-LRB-

i

-RRB-

If

the

number

of

active

NRT

users

in

the

cell

is

the

same

for

each

priority

class

i.e.

NRT

i

NRT

j

i

j

the

weights

w

-LRB-

new

-RRB-

i

should

be

set

according

to

the

basic

weights

w

i

for

i

P

-LRB-

ii

-RRB-

Priority

classes

with

low

population

of

users

compared

to

other

classes

should

be

prioritized

i.e.

the

corresponding

weights

should

be

increased

-LRB-

iii

-RRB-

The

relative

ordering

of

the

weights

should

be

preserved

in

a

strong

way

i.e.

w

-LRB-

new

-RRB-

i

-LRB-

w

i

w

i

-RRB-

w

-LRB-

new

-RRB-

i

for

i

P

Premise

-LRB-

i

-RRB-

constitutes

the

key

of

the

update

strategy

If

all

priority

classes

have

the

same

population

of

users

the

scheduling

should

work

as

in

the

case

without

adaptive

control

of

the

weights

The

rationale

behind

premise

-LRB-

ii

-RRB-

is

to

prioritize

users

that

are

consuming

less

bandwidth

-LRB-

relative

to

their

weights

-RRB-

than

users

belonging

to

other

classes

i.e.

users

of

low

population

should

be

made

more

independent

from

the

influence

of

user

classes

with

higher

population

This

premise

constitutes

the

basic

idea

for

QoS

improvement

and

is

demon-strated

by

the

following

example

that

considers

two

priority

classes

i.e.

a

high

and

low

priority

class

In

WFQ

the

available

bandwidth

is

shared

among

all

active

users

according

to

their

weights

That

is

if

the

minority

are

high

priority

users

the

overall

bandwidth

consumed

by

these

users

will

suffer

from

a

strong

influence

of

low

priority

users

that

hold

the

majority

Therefore

increasing

the

weights

for

high

priority

users

will

result

in

a

higher

QoS

for

this

user

class

Updating

the

weights

according

to

this

strategy

will

result

in

a

scheduling

algorithm

somewhere

between

a

WFQ

and

a

class

based

queueing

scheduler

In

fact

the

benefit

of

both

is

utilized

the

fair

sharing

of

the

bandwidth

of

WFQ

and

the

higher

bandwidth

guarantees

for

each

priority

class

provided

by

a

class

based

queueing

scheduler

Preserving

the

relative

ordering

of

the

weights

-LRB-

i.e.

premise

-LRB-

iii

-RRB-

-RRB-

guarantees

that

QoS

for

higher

priority

users

and

therefore

the

provider

revenue

can

only

be

improved

due

to

the

adaptive

control

of

the

weights

If

the

intention

of

the

update

strategy

is

not

primary

on

improving

provider

revenue

the

weights

can

be

also

set

in

a

weak

relation

i.e.

w

-LRB-

new

-RRB-

i

w

-LRB-

new

-RRB-

i

This

might

be

useful

to

increase

QoS

for

users

of

low

population

independent

of

their

priority

class

With

the

following

algorithm

the

computation

of

the

weights

w

-LRB-

new

-RRB-

w

-LRB-

new

-RRB-

P

can

be

performed

iteratively

in

P

minimum

calculations

The

iteration

is

given

by

w

-LRB-

new

-RRB-

w

-LRB-

NRT

-RRB-

-LRB-

-RRB-

w

-LRB-

new

-RRB-

i

min

w

i

w

i

w

-LRB-

new

-RRB-

i

w

i

-LRB-

NRT

i

-RRB-

i

P.

-LRB-

-RRB-

In

order

to

smooth

the

influence

of

the

number

of

NRT

users

on

the

queueing

weights

an

exponent

is

considered

-LRB-

e.g.

-RRB-

It

is

easy

to

show

that

premises

-LRB-

i

-RRB-

-LRB-

ii

-RRB-

and

-LRB-

iii

-RRB-

hold

for

the

weights

set

according

to

equations

-LRB-

-RRB-

and

-LRB-

-RRB-

The

iteration

starts

with

setting

w

-LRB-

new

-RRB-

according

to

NRT

and

continues

up

to

w

-LRB-

new

-RRB-

P

Note

that

this

is

only

one

possibility

to

set

the

new

weights

Any

other

starting

position

for

the

iteration

is

possible

and

results

in

a

slightly

different

update

of

the

weights

Nevertheless

the

algorithms

work

in

a

similar

way

and

therefore

we

consider

only

the

iteration

of

-LRB-

-RRB-

and

-LRB-

-RRB-

If

currently

no

users

of

priority

i

are

in

the

cell

i.e.

NRT

i

the

algorithm

skips

the

setting

of

the

corresponding

weight

w

-LRB-

new

-RRB-

i

and

the

next

iteration

step

i

is

related

to

step

i

Subsequently

these

weights

were

set

to

zero

For

other

scheduling

disciplines

like

weighted

round

robin

or

a

class

based

queueing

corresponding

update

strategies

can

be

derived

in

a

similar

way

Considering

advanced

pricing

policies

In

pricing

policy

-LRB-

b

-RRB-

introduced

above

users

have

to

pay

an

additional

fee

depending

on

the

throughput

improvement

due

to

the

update

of

the

queueing

weights

This

concept

of

pricing

indicates

strong

similarities

to

the

congestion-sensitive

pricing

of

the

smart

market

scheme

-LSB-

-RSB-

where

the

actual

price

for

each

packet

is

determined

based

on

the

current

state

of

network

congestion

Similarly

in

our

throughput-based

pricing

policy

the

throughput

of

users

is

determined

by

their

willingness-to-pay

additional

costs

-LRB-

according

to

their

choice

of

priority

class

-RRB-

for

transmission

of

packets

in

a

congested

network

The

additional

payment

is

justified

because

the

throughput

for

users

of

higher

priority

will

be

maintained

even

if

more

and

more

users

of

lower

priority

attend

the

cell

i.e.

the

network

is

currently

congested

We

describe

the

relative

throughput

increase

of

priority

class

i

with

the

function

i

-LRB-

t

-RRB-

-LRB-

new

-RRB-

i

w

P

THR

i

w

i

THR

P

-LRB-

-RRB-

In

equation

-LRB-

-RRB-

THR

i

is

the

current

throughput

of

class

i

derived

from

the

corresponding

sliding

window

and

is

a

scaling

exponent

-LRB-

e.g.

-RRB-

that

has

to

be

adjusted

by

the

service

provider

for

appropriate

revenue

dimensioning

In

order

to

guarantee

that

revenue

will

be

only

improved

i

-LRB-

t

-RRB-

has

to

be

truncated

i.e.

i

-LRB-

t

-RRB-

Next

we

adjust

the

weights

according

to

an

advanced

pricing

policy

that

adopts

ideas

which

have

been

successful

in

existing

GSM

networks

In

GSM

networks

the

pricing

of

a

provided

service

is

as

follows

the

proposed

service

is

offered

based

on

a

monthly

payment

for

a

dedicated

amount

IMPROVING

QoS

AND

PROVIDER

REVENUE

IN

MOBILE

NETWORKS

of

call

time

If

a

user

has

consumed

this

amount

of

time

before

the

end

of

the

month

he

has

to

pay

for

any

further

use

of

this

service

based

on

a

time-dependent

accounting

This

idea

can

be

generalized

and

extended

towards

packet-switched

services

in

networks

Analogously

a

user

has

to

pay

a

monthly

charge

for

a

dedicated

amount

of

data

volume

which

can

be

transferred

without

further

pricing

After

using

up

this

monthly

amount

of

data

the

user

has

to

pay

for

the

desired

services

according

to

the

transferred

data

volume

-LRB-

byte-based

-RRB-

Moreover

analogous

to

GSM

networks

a

user

can

utilize

unused

data

volume

i.e.

the

unused

fraction

of

the

prepaid

monthly

amount

of

data

volume

in

subsequent

months

If

the

monthly

amount

of

data

is

unrestricted

this

pricing

would

become

a

flat-rate

pricing

and

if

there

is

no

monthly

payment

the

pricing

follows

a

usage-based

policy

Thus

our

pricing

policy

constitutes

a

hybrid

approach

of

flat-rate

and

usage-based

pricing

The

update

of

the

queueing

weights

can

now

be

extended

in

a

way

that

users

consuming

their

monthly

amount

of

data

are

served

with

a

lower

priority

than

users

currently

paying

for

their

data

transfer

Therefore

we

introduce

a

new

weight

w

corresponding

to

the

not

paying

users

The

weight

w

must

be

sorted

in

the

weights

w

w

P

and

the

iterative

update

algorithm

-LRB-

-RRB-

-LRB-

-RRB-

can

be

applied

to

the

P

weights

as

described

above

In

order

to

distinguish

not

paying

users

with

different

priorities

these

users

are

served

by

the

WFQ

scheduler

with

weights

w

w

P

relative

to

w

That

is

WFQ

is

applied

to

P

weights

i.e.

w

w

P

and

-LRB-

w

w

-RRB-

w

-LRB-

w

w

-RRB-

w

P

with

w

w

w

P

Implementation

issues

As

outlined

in

section

the

controlled

system

parameters

for

QoS

and

revenue

improvement

i.e.

b

h

w

i

i

constitute

an

integral

component

of

the

proposed

extension

to

a

BSC

The

adjustment

of

system

parameters

is

only

based

on

implicit

information

that

is

directly

measured

by

the

online

monitoring

component

Therefore

no

additional

signaling

with

other

BSCs

is

necessary

for

updating

system

parameters

The

online

monitored

QoS

measures

i.e.

PLP

HFP

CBP

NRT

i

and

THR

i

can

easily

be

derived

and

stored

within

the

BSC

-LRB-

see

figure

-RRB-

The

PLP

can

directly

be

determined

by

counting

the

number

of

IP

packets

which

are

lost

due

to

buffer

overflow

in

the

NRT

queue

HFP

as

well

as

the

CBP

is

determined

by

the

non-admitted

handover

calls

and

new

calls

in

the

admission

controller

respectively

Admission

termination

and

handover

of

NRT

calls

enable

the

profiling

of

NRT

i

the

number

of

non

real-time

sessions

with

priority

i.

Moreover

the

packet

scheduler

allows

the

throughput

computation

of

NRT

users

according

to

their

individual

priorities

Furthermore

no

time

consuming

signaling

is

needed

to

transfer

the

system

pattern

inside

the

BSC

because

the

online

performance

monitoring

component

and

the

performance

management

entity

both

reside

in

the

BSC

The

question

arises

how

call

charging

can

be

accomplished

for

the

considered

pricing

policies

in

mobile

networks

For

pricing

policy

-LRB-

a

-RRB-

i.e.

a

fixed

payment

per

kbit

call

charging

can

easily

be

processed

by

the

subscription

management

component

of

the

operation

subsystem

-LRB-

OSS

-RRB-

by

means

of

the

call

charging

mechanism

using

the

home

location

register

-LRB-

HLR

-RRB-

-LSB-

-RSB-

Similarly

the

hybrid

pricing

scheme

can

be

realized

except

that

the

remaining

amount

of

prepaid

data

volume

has

to

be

stored

in

the

HLR

for

charging

the

transferred

data

volume

Utilizing

these

existing

charging

mechanisms

no

additionally

signaling

overhead

arises

for

charging

data

services

The

throughput-based

pricing

policy

-LRB-

pricing

policy

-LRB-

b

-RRB-

-RRB-

just

slightly

changes

the

situation

and

can

easily

be

implemented

within

the

BSC

using

a

local

copy

of

the

user

's

HLR

charging

data

fields

This

local

data

minimizes

signaling

overhead

of

individual

user

charging

According

to

the

transferred

data

volume

and

current

throughput

of

the

user

's

bandwidth

class

this

local

charging

profile

is

continuously

updated

Handovers

with

changing

BSC

of

response

induce

the

transfer

of

this

local

charging

profile

to

the

new

BSC

of

response

Subsequently

these

local

data

have

to

be

updated

in

the

HLR

for

individual

user

accounting

after

termination

of

the

call

Note

that

this

transfer

of

local

charging

profiles

can

naturally

be

embedded

in

the

OSS

functionality

For

traffic

modeling

of

RT

applications

we

utilize

the

approach

proposed

in

-LSB-

-RSB-

where

variable

bit

rate

video

traffic

is

modeled

in

terms

of

time-discrete

M/G

input

processes

This

model

is

based

on

measured

video

streams

and

efficiently

captures

the

correlation

structure

of

the

considered

video

traffic

applying

the

time-discrete

M/G

input

process

The

generated

traffic

is

transformed

utilizing

a

hybrid

Gamma/Pareto

numerical

transformation

in

order

to

capture

the

marginal

distribution

of

the

measured

traffic

stream

Subsequently

the

synthetically

generated

traffic

is

broken

down

to

IP

packets

of

a

maximum

size

of

bytes

which

are

uniformly

distributed

within

a

given

frame-duration

of

the

MPEG

video

sequence

comprising

of

s.

Note

that

this

traffic

model

does

not

propose

information

for

modeling

RT

session

durations

Therefore

we

assume

session

durations

to

be

exponentially

distributed

-LRB-

see

section

-RRB-

Recent

recommendations

for

modeling

NRT

traffic

and

analytical

traffic

models

for

mobile

networks

are

proposed

in

-LSB-

-RSB-

respectively

The

traffic

model

is

based

on

real

measurements

conducted

at

an

Internet

service

provider

dial-in

link

which

comprises

comparable

characteristics

of

future

mobile

networks

-LSB-

-RSB-

i.e.

different

access

speeds

influence

of

the

user

behavior

due

to

different

tariff

limits

as

well

as

asymmetric

up

and

downlink

traffic

Based

on

these

measurements

a

NRT

traffic

model

is

conducted

applying

the

idea

of

the

single

user

traffic

model

which

describes

traffic

characteristics

on

session-level

connection-level

i.e.

application-level

and

packet-level

respectively

The

key

insight

of

this

modeling

approach

lies

in

an

appropriate

scaling

procedure

of

C.

LINDEMANN

ET

AL.

Table

Characteristics

for

different

UMTS

session

types

Circuit

switched

Streaming

real

time

-LRB-

RT

-RRB-

Interactive

non

real

time

-LRB-

NRT

-RRB-

voice

service

Audio

Video

high

priority

normal

priority

low

priority

Portion

of

arriving

requests

Session

duration

s

s

determined

by

session

volume

distribution

Session

dwell

time

s

s

s

the

measured

trace

data

towards

typical

bandwidth

classes

of

mobile

networks

i.e.

kbps

kbps

and

kbps

In

this

context

a

bandwidth

class

denotes

the

maximum

bandwidth

capability

of

future

handheld

devices

We

refer

to

-LSB-

-RSB-

for

details

of

the

NRT

traffic

model

especially

for

the

parameterization

of

the

traffic

characteristics

The

simulation

environment

In

order

to

evaluate

the

proposed

approach

for

adaptive

control

we

developed

a

simulation

environment

for

a

UMTS

access

network

i.e.

a

UMTS

Terrestrial

Radio

Access

Network

-LRB-

UTRAN

-LSB-

-RSB-

-RRB-

The

simulator

considers

a

cell

cluster

comprising

of

seven

hexagonal

cells

with

corresponding

transceiver

stations

-LRB-

i.e.

Node

B

elements

-RRB-

that

are

managed

by

a

base

station

controller

-LRB-

i.e.

a

Radio

Network

Controller

RNC

-RRB-

We

assume

that

a

mobile

user

requests

a

new

session

in

a

cell

according

to

a

Poisson

process

When

a

mobile

user

starts

a

new

session

the

session

is

classified

as

voice

RT

or

NRT

session

i.e.

with

the

session

the

user

utilizes

voice

RT

or

NRT

services

mutually

exclusive

RT

sessions

consist

of

streaming

downlink

traffic

corresponding

to

the

UMTS

streaming

class

specified

by

-LSB-

-RSB-

and

NRT

sessions

consist

of

elastic

traffic

and

correspond

to

the

UMTS

interactive

class

or

background

class

respectively

For

the

year

an

amount

of

about

voice

calls

is

anticipated

-LSB-

-RSB-

We

assume

that

one

half

of

the

voice

calls

are

served

over

the

frequency

spectrum

for

traditional

GSM

services

-LRB-

i.e.

and

MHz

-RRB-

and

the

second

half

is

served

over

the

new

frequency

spectrum

allocated

for

UMTS

Nevertheless

the

simulator

considers

only

the

new

frequency

spectrum

Therefore

we

assume

that

of

the

call

requests

are

voice

calls

whereas

RT

and

NRT

sessions

constitute

and

of

the

overall

arriving

requests

-LRB-

see

table

-RRB-

Subsequently

we

have

to

specify

the

QoS

profile

for

RT

and

NRT

sessions

For

RT

sessions

the

simulator

considers

two

QoS

profiles

i.e.

a

low

bandwidth

profile

comprising

of

a

guaranteed

bit

rate

of

kbps

corresponding

to

streaming

audio

and

a

high

bandwidth

profile

comprising

of

a

guaranteed

bit

rate

of

kbps

corresponding

to

streaming

video

According

to

the

RT

traffic

model

presented

in

section

we

assume

that

of

the

RT

sessions

utilize

the

low

bandwidth

profile

whereas

the

remaining

utilize

the

high

bandwidth

profile

Following

the

single

user

traffic

model

NRT

sessions

are

partitioned

according

to

different

bandwidth

classes

as

follows

for

kbps

for

kbps

and

for

kbps

comprising

of

different

priorities

-LRB-

see

table

-RRB-

respectively

The

amount

of

time

that

a

mobile

user

with

an

ongoing

session

remains

within

the

cell

is

called

dwell

time

If

the

session

is

still

active

after

the

dwell

time

a

handover

toward

an

adjacent

cell

takes

place

The

call/session

duration

is

defined

as

the

amount

of

time

that

the

call

will

be

active

assuming

it

completes

without

being

forced

to

terminate

due

to

handover

failure

We

assume

the

duration

of

voice

calls

and

RT

sessions

to

be

exponentially

distributed

As

proposed

in

-LSB-

-RSB-

the

dwell

time

is

modeled

by

a

lognormal

distribution

All

corresponding

mean

values

are

shown

in

table

A

NRT

session

remains

active

until

a

specific

data

volume

drawn

according

to

a

bandwidth-dependent

lognormal

distribution

is

transferred

To

distinguish

between

NRT

traffic

classes

the

UMTS

simulator

implements

a

WFQ

scheduler

with

three

packet

priorities

-LRB-

high

-RRB-

-LRB-

normal

-RRB-

and

-LRB-

low

-RRB-

with

weights

w

w

and

w

These

priorities

correspond

to

the

traffic

handling

priority

specified

by

To

model

the

user

behavior

in

the

cell

the

simulator

considers

the

handover

flow

of

active

mobile

users

from

adjacent

cells

The

iterative

procedure

introduced

in

-LSB-

-RSB-

is

employed

for

balancing

the

incoming

and

outgoing

handover

rates

The

iteration

is

based

on

the

assumption

that

the

incoming

handover

rate

of

a

user

class

at

step

i

is

equal

to

the

corresponding

outgoing

handover

rate

computed

at

step

i.

UMTS

system

model

assumptions

The

simulator

exactly

mimics

UMTS

system

behavior

on

the

IP

level

The

focus

is

not

on

studying

link

level

dynamics

Therefore

we

assume

a

reliable

link

layer

as

provided

by

the

automatic

repeat

request

-LRB-

ARQ

-RRB-

mechanism

of

the

Radio

Link

Control

-LRB-

RLC

-RRB-

protocol

As

shown

in

-LSB-

-RSB-

for

the

General

Packet

Radio

Service

-LRB-

GPRS

-RRB-

the

ARQ

mechanism

is

fast

enough

to

recover

from

packet

losses

before

reliable

protocols

on

higher

layers

-LRB-

e.g.

TCP

-RRB-

recognize

these

losses

due

to

timer

expiration

Thus

a

reliable

link

level

can

be

assumed

when

considering

higher

layer

protocol

actions

-LRB-

see

e.g.

-LSB-

-RSB-

-RRB-

To

accurately

model

the

UMTS

radio

access

network

the

simulator

represents

the

functionality

of

one

radio

network

controller

and

seven

Node

B

transceiver

stations

one

for

each

of

the

considered

cells

Since

in

the

end-to-end

path

the

wireless

link

is

typically

the

bottleneck

and

given

the

anticipated

traffic

asymmetry

the

simulator

focuses

on

resource

contention

in

the

downlink

-LRB-

i.e.

the

path

RNC

Node

B

MS

-RRB-

of

the

radio

interface

The

simulator

considers

the

UTRAN

access

scheme

based

on

Wideband-Code

Division

Multiple

Access

-LRB-

W-CDMA

-RRB-

in

Frequency

Division

Duplex

mode

-LRB-

FDD

-RRB-

proposed

by

-LSB-

-RSB-

In

FDD

downlink

a

division

of

the

radio

frequencies

into

IMPROVING

QoS

AND

PROVIDER

REVENUE

IN

MOBILE

NETWORKS

four

physical

code

channels

with

data

rates

of

kbps

each

up

to

physical

code

channels

with

kbps

data

rates

each

is

possible

Therefore

the

overall

bandwidth

that

is

available

in

one

cell

is

kbps

For

the

channel

coding

we

assume

a

convolution-coding

scheme

with

coding

factor

In

the

experiments

without

adaptive

control

the

handover

bandwidth

portion

b

h

is

and

the

NRT

queue

threshold

is

set

to

The

simulation

environment

was

implemented

using

the

simulation

library

CSIM

-LSB-

-RSB-

In

a

presimulation

run

the

handover

flow

is

balanced

for

each

cell

at

the

boundary

of

the

seven-cell

cluster

All

simulation

results

are

derived

with

confidence

level

of

using

the

batch

means

method

The

execution

of

a

single

simulation

run

requires

about

min

of

CPU

time

-LRB-

depending

on

the

call

arrival

rate

-RRB-

on

a

dual

processor

Sun

Sparc

Enterprise

with

one

GByte

main

memory

Implementation

of

the

hybrid

pricing

policy

in

the

simulator

According

to

the

hybrid

pricing

policy

as

introduced

in

section

the

user

's

overall

remaining

amount

of

prepaid

data

volume

d

out

of

the

user

's

monthly

data

volume

D

is

determined

at

the

beginning

of

a

session

Moreover

the

remaining

amount

of

data

volume

of

previous

months

r

is

determined

For

simulation

study

purposes

this

is

accomplished

by

choosing

the

random

value

d

uniformly

out

of

the

interval

-LSB-

kD

-RSB-

kD

captures

the

monthly

amount

of

data

a

user

typically

transfers

i.e.

a

user

typically

transfers

a

multiple

k

of

the

data

volume

D

that

is

available

for

a

fixed

monthly

payment

The

random

value

r

is

sampled

according

to

a

uniform

distribution

out

of

the

interval

-LSB-

D

-RSB-

where

D

measures

the

maximum

amount

of

unused

data

volume

of

previous

months

If

d

exceeds

D

r

the

user

has

no

remaining

prepaid

data

volume

including

the

data

volume

of

the

current

and

the

previous

months

Otherwise

there

is

a

remaining

amount

of

prepaid

data

volume

D

r

d

for

the

considered

user

and

additional

pricing

arises

only

if

the

transferred

data

volume

of

the

user

session

exceeds

D

r

d.

Thus

during

the

user

session

the

remaining

data

volume

has

to

be

updated

according

to

the

actually

transferred

data

In

the

simulation

studies

we

utilize

the

proposed

hybrid-pricing

scheme

with

a

prepaid

monthly

data

volume

of

MB

According

to

the

different

priority

classes

and

the

volume-based

pricing

for

transferred

data

exceeding

the

prepaid

monthly

data

volume

comprises

of

and

cost-units

per

MB

respectively

Considering

the

changing

traffic

loads

according

to

the

daytime

this

approach

can

be

refined

by

the

notion

of

different

pricing

for

daily

periods

of

time

For

the

parameterization

of

the

typically

monthly

transferred

data

volume

we

assume

k

Note

that

the

parameterization

of

the

pricing

scheme

is

chosen

for

demonstration

purposes

only

Due

to

the

high

flexibility

of

the

hybrid

pricing

scheme

it

can

be

easily

extended

towards

multiple

concurrent

pricing

schemes

comprising

of

e.g.

different

monthly

amounts

of

prepaid

data

volumes

different

payments

for

the

individual

priority

classes

or

a

pure

usage-based

pricing

as

well

as

pure

flat-rate

pricing

-LRB-

a

-RRB-

-LRB-

b

-RRB-

Figure

Impact

of

adaptive

performance

management

on

non

real-time

traffic

Performance

results

Using

simulation

experiments

we

illustrate

the

benefit

of

the

proposed

unified

approach

for

adaptive

performance

management

of

UMTS

systems

In

particular

we

show

the

improvement

of

QoS

measures

and

the

increase

in

revenue

earned

by

service

providers

The

presented

curves

plot

the

mean

values

of

the

confidence

intervals

for

the

considered

QoS

measures

In

almost

all

figures

the

overall

call/session

arrival

rate

of

new

mobile

users

is

varied

to

study

the

cell

under

increasing

load

conditions

For

ease

of

notation

results

with

and

without

adaptive

performance

management

-LRB-

APM

-RRB-

are

abbreviated

by

APM

on

and

APM

off

respectively

In

a

first

experiment

we

investigate

the

effect

of

adaptive

control

on

the

threshold

for

the

buffer

size

of

the

NRT

queue

denoted

by

Figure

shows

the

NRT

packet

loss

probability

-LRB-

a

-RRB-

and

the

average

number

of

NRT

users

in

the

cell

-LRB-

b

-RRB-

for

the

UMTS

system

with

and

without

adaptive

control

Furthermore

the

figures

distinguish

between

different

desired

loss

levels

as

introduced

in

section

We

observe

that

the

APM

achieves

a

substantially

decrease

in

packet

loss

probability

Moreover

the

packet

loss

probability

can

be

kept

below

a

constant

level

for

increasing

arrival

rates

of

mobile

users

Note

that

this

level

slightly

differs

from

the

desired

level

of

the

QoS

measure

This

is

due

to

the

fact

that

the

update

function

only

decreases

the

NRT

threshold

if

the

online

C.

LINDEMANN

ET

AL.

Figure

Number

of

packet

losses

for

a

half

day

window

of

a

weekly

usage

pattern

measured

packet

loss

probability

is

greater

than

Therefore

the

packet

loss

probability

is

in

steady

state

also

slightly

greater

than

Nevertheless

figure

shows

that

the

resulting

packet

loss

probability

can

be

adjusted

quite

well

For

very

low

arrival

rates

the

packet

loss

probability

is

increased

compared

to

the

case

without

adaptive

control

This

is

because

the

packet

loss

probability

is

below

the

desired

level

and

is

adjusted

towards

Figure

-LRB-

b

-RRB-

shows

the

average

number

of

NRT

users

admitted

in

the

cell

For

all

curves

the

number

of

NRT

users

in

the

cell

first

increases

up

to

about

users

for

an

arrival

rate

of

arrivals

per

second

For

higher

arrival

rates

the

admission

controller

decides

to

reject

requests

depending

on

the

choice

of

the

NRT

threshold

In

the

case

without

APM

the

number

of

NRT

users

approaches

whereas

in

the

cases

with

adaptive

control

less

users

are

admitted

in

the

cell

because

the

threshold

parameter

is

decreased

-LRB-

e.g.

about

users

for

-RRB-

For

high

arrival

rates

a

slightly

decrease

of

the

average

number

of

NRT

users

can

be

observed

This

is

due

to

the

fact

that

with

increasing

arrival

rate

the

competition

between

voice

RT

and

NRT

traffic

decreases

the

bandwidth

capacity

available

for

NRT

traffic

Therefore

less

NRT

users

are

admitted

In

the

experiment

presented

in

figure

we

study

the

absolute

number

of

packet

losses

observed

in

one

hour

for

a

transient

scenario

i.e.

the

arrival

rate

of

new

calls

is

changing

every

hour

according

to

a

half

day

window

of

a

weekly

usage

pattern

-LSB-

-RSB-

The

purpose

of

this

experiment

is

to

show

that

the

adaptive

performance

management

is

fast

enough

to

react

on

changing

traffic

conditions

i.e.

to

effectively

adjust

the

NRT

threshold

in

order

to

reduce

packet

losses

The

bars

shown

in

figure

correspond

to

the

number

of

packet

losses

for

experiments

with

and

without

adaptive

control

Furthermore

the

figure

distinguishes

between

a

desired

loss

level

of

and

respectively

The

new

call

arrival

rates

considered

in

one

hour

are

depicted

above

the

bars

We

conclude

from

figure

that

for

a

real-life

pattern

of

changing

arrival

rates

the

packet

losses

can

be

effectively

controlled

by

the

APM

This

justifies

the

choice

of

the

gradient

m

in

the

update

function

for

the

NRT

threshold

-LRB-

a

-RRB-

-LRB-

b

-RRB-

Figure

Impact

of

adaptive

performance

management

on

handover

traffic

Next

we

study

the

effect

of

the

APM

on

the

handover

traffic

Figure

shows

the

handover

failure

probability

-LRB-

a

-RRB-

and

the

new

call

blocking

probability

-LRB-

b

-RRB-

for

the

UMTS

system

with

and

without

APM

Similar

to

figure

we

distinguish

between

different

desired

levels

for

the

handover

failure

probability

The

desired

level

for

new

call

blocking

is

fixed

to

Note

that

for

controlling

the

handover

bandwidth

the

desired

level

can

be

used

only

to

adjust

the

degree

of

prioritization

of

handover

failure

over

new

call

blocking

Distinct

from

the

packet

loss

probability

it

can

not

be

expected

to

keep

the

handover

failure

probability

at

a

constant

level

for

increasing

traffic

load

That

is

for

two

reasons

-LRB-

-RRB-

the

handover

bandwidth

is

adjusted

according

to

two

QoS

measures

that

have

a

contrary

influence

and

-LRB-

-RRB-

the

increase

of

the

handover

bandwidth

must

be

limited

by

a

certain

portion

of

the

overall

available

bandwidth

-LRB-

see

section

-RRB-

If

this

limit

is

reached

handover

failures

occur

more

frequently

for

further

increasing

call

arrival

rate

These

two

effects

can

be

observed

in

the

curves

of

figure

Nevertheless

the

handover

failure

probability

is

improved

more

than

one

order

of

magnitude

for

call

arrival

rates

between

and

call

requests

per

second

and

a

desired

loss

level

When

studying

the

blocking

probability

of

new

voice

calls

and

RT

sessions

-LRB-

see

figure

-LRB-

b

-RRB-

-RRB-

we

surly

observe

a

higher

blocking

probability

of

new

calls

in

the

case

with

adaptive

control

and

high

arrival

rate

In

IMPROVING

QoS

AND

PROVIDER

REVENUE

IN

MOBILE

NETWORKS

Figure

Improving

QoS

for

high

priority

non

real-time

users

Figure

Effect

of

adjusting

WFQ

weights

on

bandwidth

utilization

of

NRT

traffic

fact

almost

all

call

requests

are

blocked

if

system

load

is

high

In

a

next

experiment

we

study

the

impact

of

NRT

users

on

QoS

by

the

adaptive

control

of

the

queueing

weights

as

introduced

in

section

Figure

plots

the

average

throughput

per

user

for

each

priority

class

of

NRT

traffic

As

shown

in

table

we

assume

NRT

users

with

high

priority

with

normal

priority

and

with

low

priority

Recall

that

higher

priority

service

is

more

expensive

and

hence

more

users

choose

low

priority

service

If

the

overall

load

in

the

cell

is

very

low

-LRB-

i.e.

less

than

call

arrivals

per

second

-RRB-

each

NRT

user

receives

the

maximal

throughput

independent

of

the

priority

class

However

when

the

cell

load

is

further

increased

-LRB-

arrival

rates

of

more

than

arrivals

per

second

-RRB-

throughput

for

users

of

all

priority

classes

decreases

The

intention

of

adaptively

controlling

the

queueing

weights

is

to

reduce

heavy

throughput

degradation

of

high

priority

users

in

this

case

The

performance

increase

of

high

priority

users

and

the

decrease

of

low

priority

users

are

shown

in

figure

Figure

plots

the

bandwidth

portion

utilized

for

each

priority

class

of

NRT

traffic

For

low

arrival

rate

-LRB-

i.e.

less

than

call

arrivals

per

second

-RRB-

NRT

users

with

low

priority

utilize

the

greatest

portion

of

the

NRT

bandwidth

because

most

NRT

users

have

priority

low

When

the

cell

load

is

increased

-LRB-

arrival

rates

of

more

than

arrivals

per

second

-RRB-

the

band

-LRB-

a

-RRB-

-LRB-

b

-RRB-

Figure

Revenue

improvement

for

usage-based

pricing

policy

width

will

be

utilized

more

and

more

by

high

priority

users

The

adaptive

control

of

the

WFQ

weights

decides

to

intensify

this

effect

because

users

belonging

to

priority

high

suffer

from

the

high

population

of

low

priority

users

Figures

and

are

derived

from

simulation

runs

with

-LRB-

see

section

-RRB-

In

the

following

experiments

we

study

the

impact

of

controlling

the

queueing

weights

on

the

revenue

function

-LRB-

see

equation

-LRB-

-RRB-

-RRB-

for

the

three

proposed

pricing

policies

i.e.

usage-based

usage

throughput-based

and

the

hybrid

pricing

policy

From

the

revenue

function

the

average

-LRB-

steady

state

-RRB-

provider

revenue

in

the

considered

cell

can

be

derived

Recall

that

the

available

bandwidth

for

NRT

traffic

is

variable

for

different

call

arrival

rates

Therefore

we

consider

the

revenue

earned

by

the

provider

in

one

hour

per

available

bandwidth

unit

i.e.

per

available

kbit

for

NRT

traffic

Figure

shows

the

provider

revenue

for

the

usage-based

pricing

policy

-LRB-

i.e.

-RRB-

and

different

values

of

the

exponent

As

discussed

in

section

the

best

revenue

improvement

will

be

achieved

with

priority

queueing

From

the

curves

we

conclude

that

the

update

strategy

increases

the

revenue

in

one

cell

successfully

for

the

considered

traffic

assumptions

Recall

that

the

revenue

improvement

stems

from

a

shift

in

bandwidth

utilization

towards

higher

priority

users

-LRB-

see

figure

-RRB-

if

the

population

of

high

priority

users

is

low

compared

to

users

of

lower

priority

Figure

-LRB-

b

-RRB-

shows

the

revenue

improvement

for

different

user

populations

In

the

experiment

the

percentage

of

high

C.

LINDEMANN

ET

AL.

Figure

Revenue

improvement

for

usage

throughput-based

pricing

policy

Figure

Revenue

improvement

for

hybrid

pricing

policy

priority

users

among

the

arriving

user

requests

is

varied

The

remaining

users

are

assumed

to

be

low

priority

users

Normal

priority

users

are

not

considered

in

this

experiment

-LRB-

i.e.

normal

priority

users

-RRB-

This

figure

shows

how

the

adaptive

control

of

the

queueing

weights

works

As

expected

for

a

low

percentage

of

high

priority

users

the

corresponding

weight

is

increased

Therefore

QoS

for

high

priority

users

and

the

provider

revenue

is

also

increased

For

more

than

high

priority

users

the

revenue

is

the

same

as

in

the

case

without

adaptive

control

No

further

revenue

improvement

is

allowed

because

degradation

of

QoS

for

low

priority

users

would

be

unacceptable

Considering

a

weak

relation

among

the

weights

as

introduced

in

section

would

decrease

the

revenue

compared

to

the

case

without

adaptive

control

for

more

than

high

priority

users

This

might

be

useful

to

increase

QoS

for

users

of

low

population

independent

of

their

priority

class

Figure

shows

the

revenue

improvement

for

the

usage

throughput-based

pricing

policy

and

scaling

exponents

and

In

the

last

experiment

we

studied

the

revenue

improvement

for

the

hybrid

pricing

policy

-LRB-

see

figure

-RRB-

We

assume

that

half

of

the

arriving

users

start

their

session

in

non-paying

mode

-LRB-

i.e.

k

-RRB-

The

curves

distinguish

between

weights

w

and

w

for

the

non-paying

users

Furthermore

the

revenue

for

the

case

with

and

without

adaptive

control

is

compared

The

curves

are

derived

from

simulations

with

and

From

the

revenue

curves

of

figures

the

average

monthly

revenue

can

be

computed

considering

a

daily/weekly

usage-pattern

and

different

splits

of

call

arrival

rates

of

users

requesting

different

services

-LRB-

i.e.

voice

RT

NRT

with

different

priorities

-RRB-

Comparing

the

monthly

revenue

for

the

pricing

policies

used

in

figures

with

the

monthly

revenue

for

the

hybrid

pricing

policy

a

provider

can

determine

values

such

as

the

monthly

free

data

volume

and

monthly

payment

per

user

We

introduced

a

unified

approach

based

on

a

mathematical

framework

for

the

adaptive

performance

management

of

mobile

networks

Opposed

to

previous

work

-LSB-

-RSB-

the

improvement

of

quality

of

service

-LRB-

QoS

-RRB-

and

the

optimization

of

mobile

service

provider

revenue

was

considered

in

an

integrated

way

The

unified

approach

aims

at

improving

both

QoS

for

mobile

subscribers

and

increasing

revenue

earned

by

service

providers

System

parameters

controlled

by

adaptive

performance

management

constitute

the

portion

of

bandwidth

reserved

for

handovers

the

buffer

threshold

of

the

queue

for

non

real-time

traffic

and

the

weights

of

a

weighted

fair

queueing

packet

scheduler

Using

the

UMTS

traffic

model

of

-LSB-

-RSB-

and

a

simulator

on

the

IP

level

for

the

UMTS

system

we

presented

performance

curves

for

various

QoS

measures

to

illustrate

the

benefit

of

the

unified

approach

for

adaptive

performance

management

We

introduced

update

functions

that

effectively

control

the

packet

loss

probability

and

the

handover

failure

probability

Considering

usage-based

usage

throughput-based

and

hybrid

pricing

policies

we

showed

that

the

provider

revenue

in

one

cell

can

be

significantly

increased

by

the

adaptive

control

of

the

queueing

weights

Throughout

the

paper

we

considered

the

services

and

QoS

profiles

standardized

for

UMTS

Thus

the

proposed

approach

for

adaptive

control

is

tailored

to

UMTS

networks

However

by

considering

other

services

and

QoS

profiles

the

basic

ideas

underlying

the

unified

approach

for

adaptive

performance

management

can

also

be

applied

for

the

adaptive

control

of

other

kinds

of

multi-service

IP

networks

-LSB-

-RSB-

-LSB-

-RSB-

QoS

concept

and

architecture

Technical

Specification

TS

-LRB-

September

-RRB-

-LSB-

-RSB-

UTRAN

overall

description

Technical

Specification

TS

-LRB-

September

-RRB-

-LSB-

-RSB-

M.

Ajmone

Marsan

S.

Marano

C.

Mastroianni

and

M.

Meo

Performance

analysis

of

cellular

mobile

communication

networks

supporting

multimedia

services

Mobile

Networks

and

Applications

-LRB-

-RRB-

-LSB-

-RSB-

K.

Aretz

M.

Haardt

W.

Konh

user

and

W.

Mohr

The

future

of

wireless

communications

beyond

the

third

generation

Computer

Networks

-LRB-

-RRB-

-LSB-

-RSB-

F.

Barcel

and

J.

Jord

n

Channel

holding

time

distribution

in

public

cellular

telephony

in

Proceedings

of

the

International

Teletraffic

Congress

Edinburgh

Scotland

-LRB-

-RRB-

pp.

IMPROVING

QoS

AND

PROVIDER

REVENUE

IN

MOBILE

NETWORKS

-LSB-

-RSB-

The

Simulation

Engine

-LSB-

-RSB-

S.K.

Das

R.

Jayaram

N.K.

Kakani

and

S.K.

Sen

A

call

admission

and

control

scheme

for

Quality-of-Service

provisioning

in

next

generation

wireless

networks

Wireless

Networks

-LRB-

-RRB-

-LSB-

-RSB-

A.

Demers

S.

Keshav

and

S.

Shenker

Analysis

and

simulation

of

a

fair

queueing

algorithm

in

Proceedings

of

the

International

Symposium

on

Communications

Architectures

and

Protocols

-LRB-

SIGCOMM

-RRB-

Austin

TX

-LRB-

-RRB-

pp.

-LSB-

-RSB-

S.

Floyd

and

V.

Jacobson

Link-sharing

and

resource

management

models

for

packet

networks

IEEE/ACM

Transactions

on

Networking

-LRB-

-RRB-

-LSB-

-RSB-

X.

Geng

and

A.B.

Whinston

Profiting

from

value-added

wireless

services

IEEE

Computer

-LRB-

August

-RRB-

-LSB-

-RSB-

A.

Gupta

D.O.

Stahl

and

A.B.

Whinston

The

economics

of

network

management

Communications

of

the

ACM

-LRB-

-RRB-

-LSB-

-RSB-

A.

Gupta

D.O.

Stahl

and

A.B.

Whinston

Priority

pricing

of

integrated

services

networks

in

Internet

Economics

eds

L.

McKnight

and

J.

Bailey

-LRB-

MIT

Press

-RRB-

pp.

-LSB-

-RSB-

G.

Haring

R.

Marie

and

K.S.

Trivedi

Loss

formulas

and

their

application

to

optimization

for

cellular

networks

IEEE

Transactions

on

Vehicular

Technology

-LRB-

-RRB-

-LSB-

-RSB-

A.

Klemm

C.

Lindemann

and

M.

Lohmann

Traffic

modeling

and

characterization

for

UMTS

networks

in

Proceedings

of

GLOBECOM

San

Antonio

TX

-LRB-

November

-RRB-

pp.

-LSB-

-RSB-

A.

Klemm

C.

Lindemann

and

M.

Lohmann

Traffic

modeling

of

IP

networks

using

the

batch

Markovian

arrival

process

in

Proceedings

of

Tools

London

Great

Britain

-LRB-

April

-RRB-

pp.

-LSB-

-RSB-

J.

Kilpi

and

I.

Norros

Call

level

traffic

analysis

of

a

large

ISP

in

Proceedings

of

the

ITC

Specialist

Seminar

on

Measurement

and

Modeling

of

IP

Traffic

Monterey

CA

-LRB-

-RRB-

pp.

-LSB-

-RSB-

M.

Krunz

and

A.

Makowski

A

source

model

for

VBR

video

traffic

based

on

M/G

input

processes

in

Proceedings

of

the

Conference

on

Computer

Communications

-LRB-

IEEE

INFOCOM

-RRB-

San

Francisco

CA

-LRB-

-RRB-

pp.

-LSB-

-RSB-

C.

Lindemann

M.

Lohmann

and

A.

Th

mmler

Adaptive

performance

management

for

UMTS

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Networks

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R.

Ludwig

A.

Konrad

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Joseph

Optimizing

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links

in

Proceedings

of

the

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on

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MobiCom

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Seattle

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pp.

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MacKie-Mason

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MIT

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Meyer

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performance

over

GPRS

in

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of

the

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-LRB-

IEEE

WCNC

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Orleans

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-RRB-

pp.

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Wireless

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MWIF

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mobile

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September

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J.M.

Peha

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Sutivong

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S.

Rao

and

E.R.

Petersen

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-RSB-

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H.

Zhang

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disciplines

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-LSB-

-RSB-

Wireless

World

Research

Forum

-LRB-

WWRF

-RRB-

wireless-world-research.org

Christoph

Lindemann

is

an

Associate

Professor

in

the

Department

of

Computer

Science

at

the

University

of

Dortmund

and

leads

the

Computer

Systems

and

Performance

Evaluation

group

From

to

he

was

a

Senior

Research

Scientist

at

the

GMD

Institute

for

Computer

Architecture

and

Software

Technology

-LRB-

GMD

FIRST

-RRB-

in

Berlin

In

the

summer

and

during

the

academic

year

he

was

a

Visiting

Scientist

at

the

IBM

Almaden

Research

Center

San

Jose

CA

Christoph

Lindemann

is

a

Senior

Member

of

the

IEEE

He

is

author

of

the

monograph

Performance

Modelling

with

Deterministic

and

Stochastic

Petri

Nets

-LRB-

Wiley

-RRB-

Moreover

he

co-authored

the

survey

text

Performance

Evaluation

Origins

and

Directions

-LRB-

Springer-Verlag

-RRB-

He

served

on

the

program

committees

of

various

well-known

international

conferences

His

current

research

interests

include

mobile

computing

communication

networks

Internet

search

technology

and

performance

evaluation

E-mail

cl@cs.uni-dortmund.de

WWW

Lindemann

Marco

Lohmann

received

the

degree

Diplom-Infor-matiker

-LRB-

M.S.

in

computer

science

-RRB-

with

honors

from

the

University

of

Dortmund

in

March

Presently

he

is

a

Ph.D.

student

in

the

Computer

Systems

and

Performance

Evaluation

group

at

the

University

of

Dortmund

He

is

a

student

member

of

the

IEEE

and

the

ACM

His

research

interests

include

mobile

computing

Internet

search

technology

and

stochastic

modeling

E-mail

Axel

Th

mmler

received

the

degree

Diplom-Infor-matiker

-LRB-

M.S.

in

computer

science

-RRB-

from

the

University

of

Dortmund

in

April

Presently

he

is

a

Ph.D.

student

in

the

Computer

Systems

and

Performance

Evaluation

group

at

the

University

of

Dortmund

His

research

interests

include

mobile

computing

communication

networks

and

performance

evaluation

E-mail

adaptive performance management
provider revenue
admission control
pricing policy
3g mobile networks
packet loss probability
packet scheduler
qos

performance evaluation of next generation mobile systems