Traffic engineering in case of interconnected and integrated layers

Peter Hegyi

Tibor Cinkler

Department of Telecommunications and Media

Informatics

Budapest University of Technology and

Economics

M= EM

AAU E G

Y

E T

E

M

i

7

8 2

Namik

aengezer

Ezhan Kara an

Department of Electrical and Electronics

Engineering

Bilkent University

Joint

project of

BME

and Bilkent

University under the

U

Metro and Backbone

networks

Two

or

more layers

Different network technologies

at each layer

ovvLower Layer: Optical

o

Upper Layer: Electronic

Interoperation of different

Network Layers:

0

\Vertical interconnection

Each layer is

operated by its Control

plane

A

certain amount

of information

is shared

Overlay,

augmented,

peer

o

\Vertical integration

A

single control plane

operating both layers

U

IntroducItion

Current Study

Optical layer:

o

VWavelength

Division

Multiplexing

technology

o

Fast

lightpath

reconfigurations

(set

up/tear

down)

Electronic layer:

0

MPLS

capability

0

RSVP

with TE extensions

TE

approaches:

o TE

with Adaptive WDM

Topology

Suitable for the

integrated model

Single

provider operates both

layers

TE on

Fixed

Topology

using

MPLS

Functionalities

Overlay

model

Layers share

a

small

amount

of information

Networks

2008, Budapest

I

U

TE

With

Adaptive WDM

Topology

Use dynamic

optical layer

Adapt the lightpath topology to the changing traffic

demands

3 types of actions

to route the demands

Groom with the

traffic

on

existing

lightpaths

=

detours

increase resource

usage

Generate

new

lightpaths

=

wNastes

resources

Fragment

existing lightpaths

=

may

cause

trafic

loss

Combine these actions in the most efficient way

Route the demands

on

the Wavelength

Graph

U

TE XWith

AdaptiveWMW

Toplgy

...

Wavelength Graph Model

Nodes represented

by

sub-graphs

o

Sub-graph topology depends

on

node

functionality

o A node with

optical and electronic

interfaces

is

different

from

a

simple

OXC

Physical

links -

as

many graph edges as the

number of wavelengths

*The lightpath

set is exploited

as

far

as

possible

o do not

refuse

demands

if there's available

capacity

Avoid too many lightpath

fragmentations

Networks

2008, Budapest

5

'

I

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A

_

IN

Apply

shortest path

Link

Weights:

Transition

Cost

Edges

modeling

a

single

A in a

fiber

carrying traffic

1

Edges

modeling

a

single

A in a

fiber

without

traffic

25

Edges

modeling

transition between electronic and

optical

50

layer

carrying

traffic

Edges

modeling

transition between electronic and

optical

250

layer

without

traffic

Edges

modeling

fragmentation of existing A-paths

500

Existing

lightpaths

preferred

first

Avoid

passing to electronic layer

Avoid

deploying opto-electronic devices

Highest cost

belongs

to

fragmentation of existing lightpaths

U

MPLS TE

on

Fixed

WDMIV TopologyI

Overlay model,

separate

control

planes

Operated by

separate

providers

0

For network

management

purposes

Upper

layer designs the WDM topology and requests

from

the

lower layer

0

Use

previously

available

traffic information

Traffic deviations and changes handled by MPLS layer actions

o

LSP tunnels

set

up with Resource Reservation

Two

phases:

o

Design

of the fixed

WDM

topology

(offline)

o

Rerouting and bandwidth

update

of the LSPs

(online)

Networks

2008, Budapest

I

U

MPLS TE

on

Fixed

WDMIV TopologyI

1st

Phase: Design of the optimal WDM layer topology

Available

Information

Expected

traffic for each time interval

(an

hour)

Objective: Maximize the total routed traffic

Constraints:

Fixed number

of

lightpaths

(depends

on

the

traffic

parameters)

0

Maximum nodal

degree

Heuristic search algorithm

Utilizes Tabu

Search metaheuristic

Starts from

a

randomly

generated topology

Searches the solution

space

by consecutive

moves

*

Tear down and

existing

lightpath

and set up a new one

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...~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~...

MdPLSThnFxdWMTplg

2nd Phase:

Online traffic engineering

Rerouting

of the LSP flows

Alternate path approach

o

Precalculated paths

Use a

dynamic path

cost

function

o

Both available capacity and length are considered in the cost

function

*

When

traffic

is

low,

the

length

component dominates

*

When the

traffic

is high, the load component dominates

_______al

A,u:

Cost Function Parameters

(10,

0.5)

ost

p

L

+

A

c

C:

Lightpath capacity

pLp

:Length of path

p

CPResidual:

Residual

capacity

on

path

p

U

Flow based

Two components:

o

Expected

value

o

Zero mean

Gaussian

noise

Std.

Deviation=0.1

x

Expected value

Tact

(in j,

t=exp

(i, j, t) +

(,O

x

ep(ij,t)

To

generate expected

traffic, a 24 hour continuous

traffic

pattern

is

used

500

450-

400-350

300

250

200-

150-100

50

0

5

10

15

201

Networks 2008, Budapest

I

1 1

U

Simulations

Implementing

the

approaches

Single

LSP for each source-destination

The bandwidth requirements

of the

LSPs

are

updated

dynamically

o

Poisson process with rate 30/hour.

*When

a

bandwidth update arrives

0

,

s

approach,

(aapEFtiEve

W\/1)=

Tear down the old demand

Treat it as a

newly arriving demand

Route it on the

wavelength

graph

0

2nd

aproc

(fixed topology):

Choose the best

path

according

to

the

cost

function

(Re)route the

LSP with

new

bandwidth

on

the chosen

path

If no

sufficient capacity

o

Do not

update the bandwidth

o

Update amount is blocked

U

ul

aC

ti

su lt

IN

Traffic pattern is scaled by a factor

(Traffic magnitude)

Ratio of the maximum traffic flow to

single

wavelength

capacity

Traffic Loss Ratio

-2

10-3

I

i-4

10-5

o-6

0.4

0.45

0.5

0.55

0.6

0.65

Traffic

Magnitude

W=4,AT

W=8,AT

W=12,AT

W=4,

FT

W=8,

FT

W=12,FT

0.7

0.75

0.8

Networks

2008,

Budapest

100

Io-i

0

cn

cn

o

C,)

C.)

0

-j

0

(U

H-I

1

3

U

SimuWlation

,,

,

CA0V

R=esults

IN

Number

of

Lightpaths for

Adaptive

Topology

0.6

Traffic

Magnitude

2.5

a)

2

-(U

m

0-a)

cu

1. 5

0.8

0.4

0.7

Average

Lightpath

Length

for

Adaptive

Topology

W=4

W=6

W=8

W=12

W=16

0.45

0.5

0.55

0.6

0.65

0.7

0.75

0.8

Traffic

Magnitude

100

90

80

70

60

C,)

-.

m

Q(

CY)

2

0

a)

-Q

E

z

50

W=4

W=6

W=8

W=12

W=16

40

30

0.4

0.5

I

U

W %l

t

t

e

<

|

J

=~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~14'

o

IN

Number

of

Lightpaths for

Fixed

Topology

W=4

W=6

W=8

W=12

W=16

0.5

2.6

Average

Lightpath

Length

for Fixed

Topology

2.4

I,

2.2

CD

-J-

₂

1.8

m

.-°)

1.86

CD

a)

>1.4-1.2

0.6

Traffic

Magnitude

0.8

0.4

0.7

t',tW=4

W=6

W=8

W=12

W=16

0.45

0.5

0.55

0.6

0.65

0.7

0.75

0.8

Traffic

Magnitude

Networks

2008, Budapest

100

90

C,)

.m

Q(

a

AJ)

CY)

~0

E

z

80

70

60

50

40

30,

0.4

I

15

U

Simulation Results

IN

Used

Wavelength Resources

W=4,AT

W=8,AT

W=12,AT

W=4,FT

W=8,

FT

W=12,

FT

0.45

0.5

0.55

0.6

0.65

Traffic

Magnitude

0.7

0.75

0.8

180

?

160

a)

-

14

-Q

E

C:140

-0--- 120

@'

100

-0---

80

cm

CU

IDU)

o

_U

6

40

0.4

I

U

Conclusions

...

TE

approaches

investigated for

two

interoperation models

Adaptive Topology approach

Has better blocking

performance

Utilizes

more

network

resources

Frequent topology

changes may

delay/disrupt

traffic

(max.

40

per

hour)

Fixed Topology,

MPLS

TE

No

fast

lightpath

reconfigurations, does

not

disrupt

traffic

(D

Requires

traffic information

Future Work:

Explore

different models

More

information

sharing

Shared

intelligence

C

Layers act

collaboratively

0

Investigate the

effect physical impairments

Networks

2008, Budapest

I