Robust designs for wdm routing and provisioning
This presentation is the property of its rightful owner.
Sponsored Links
1 / 31

Robust Designs for WDM Routing and Provisioning PowerPoint PPT Presentation


  • 62 Views
  • Uploaded on
  • Presentation posted in: General

Robust Designs for WDM Routing and Provisioning. Jeff Kennington & Eli Olinick Southern Methodist University. Augustyn Ortynski & Gheorghe Spiride Nortel Networks. 1. LTE. TE. LTE. LTE. TE. LTE. LTE. TE. LTE. LTE. LTE. TE. …. …. …. …. …. …. TE. LTE. LTE. TE. LTE. LTE.

Download Presentation

Robust Designs for WDM Routing and Provisioning

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Robust designs for wdm routing and provisioning

Robust Designs for WDM Routing and Provisioning

Jeff Kennington & Eli Olinick

Southern Methodist University

Augustyn Ortynski & Gheorghe Spiride

Nortel Networks

1


Robust designs for wdm routing and provisioning

LTE

TE

LTE

LTE

TE

LTE

LTE

TE

LTE

LTE

LTE

TE

TE

LTE

LTE

TE

LTE

LTE

LTE

TE

LTE

TE

LTE

TE

LTE

TE

LTE

LTE

TE

LTE

TE

LTE

LTE

LTE

LTE

LTE

Regenerator

Optical Amplifier

Basic Building Block for the WDM Network

2


Robust designs for wdm routing and provisioning

UnderprovisionedCase

Dallas

Atlanta

Unmet demand

Satisfied demand

Excess capacity

Perfect MatchCase

LA

Phoenix

OverprovisionedCase

Boston

NYC

3


Robust designs for wdm routing and provisioning

Regret

UnderprovisionedCase

OverprovisionedCase

Underprovisioning

Overprovisioning

Perfect MatchCase

4


Robust designs for wdm routing and provisioning

Example

2

Boston

Chicago

San Francisco

3

6

7

New York

Los Angeles

5

1

4

Atlanta

Dallas

5


Robust designs for wdm routing and provisioning

Distance Matrix (KM)

6


Robust designs for wdm routing and provisioning

Scenario #1 Demand Matrix (DS3s)

7


Robust designs for wdm routing and provisioning

Scenario #2 Demand Matrix (DS3s)

8


Robust designs for wdm routing and provisioning

Scenario #3 Demand Matrix (DS3s)

9


Robust designs for wdm routing and provisioning

Scenario 1

Scenario 2

Scenario 3

Robust Solution

Figure 5. Solutions

10


Basic design model

Basic design model

Minimizecx(equip. cost)

Subject to

Ax=b(structural const)

Bx=r(demand const)

0<x<u(bounds)

xj integer for some j(integrality)

Integer Linear Program


Decision variables

Decision Variables

Scenario

s

Model

Robust Model

Variable

Description

Variables

Variables

Type

s

x

continuous

number of DS3s assigned to path

p

x

p

p

s

continuous

number of TEs assigned to node

n

?

?

n

n

s

t

contin

uous

number of TEs assigned to link

e

t

e

e

s

a

continuous

number of optical amplifiers assigned to link

e

a

e

e

s

r

continuous

number of regens assigned to link

e

r

e

e

s

f

integer

number of fibers assigned to link

e

f

e

e

s

c

integer

number of channels assigned to link

e

c

e

e

s

z

continuous

number of DS3s assigned to link

e

z

e

e

positive infeasibility for demand

(o,d)

and

+

continuous

-

z

ods

scenario

s

negative infeasibility for

demand

(o,d)

and

-

continuous

-

z

ods

scenario

s


Constants

Constants

Constant

Value or Range

Description

s

R

300

-

1500

traffic demand for pair

(o,d)

in scenario s in units of DS3s

od

TE

192

number of DS3s that

each

TE can accommodate

M

R

192

number of DS3s that each

regen can accommodate

M

A

15,360

number of DS3s that each

optical amplifier can accommodate

M

TE

50,000

unit cost for an

TE

C

R

80,000

unit cost for a regen

C

A

500,000

unit cost for an optical amplifier

C

F

24

max number of fibers available on link

e

e

max distance th

at a signal can traverse

without amplification, also

R

80km

called the reach

max number of amplified spans above which

signal regeneration is

Q

5

required

B

2

-

1106

the length of link

e

e


Routing for scenario s

Routing for scenario s


Robust model

Robust model


Robust model cont

Robust model (cont.)


Mean value model

Mean-Value model


Stochastic programming model

Stochastic Programming Model


Worst case model

Worst Case Model


Test problems overview

Source

Total Nodes

67

Total Links

107

Total Demand Pairs

200

Number of Paths/Demand

4

Total Demand Scenarios

5

Source

Total Nodes

18

Total Links

35

Total Demand Pairs

100

Number of Paths/Demand

4

Total Demand Scenarios

5

Test problems overview

  • Regional US network – DA problem

  • European multinational network – KL problem


Robust designs for wdm routing and provisioning

60

29

37

24

4

1

50

14

3

65

48

40

18

17

5

53

51

49

66

25

69

68

28

22

56

63

26

43

16

11

21

10

9

7

20

6

55

44

58

12

54

59

35

41

42

45

33

8

47

62

46

30

64

36

67

52

32

39

38

61

13

57

31

2

19

34

27

23

DA Test Problem

16


Robust designs for wdm routing and provisioning

10

Legend

1Brussels

2Copenhagen

3Paris

4Berlin

5Athens

6Dublin

7Rome

8Luxembourg

9Amsterdam

10Oslo

11Lisbon

12Madrid

13Stockholm

14Zurich

15London

16Zagreb

17Prague

18Vienna

13

2

6

4

9

15

1

8

3

17

14

18

16

7

11

12

European Problem

5

17


Da method comparison

Scenario

Prob.

TEs

Rs

As

CPU Seconds

Equipment Cost

1

0.15

24,996

3962

563

0.5

1,848,000,000

2

0.20

39,456

6502

864

0.5

2,925,000,000

3

0.30

51,882

8074

1101

0.5

3,791,000,000

4

0.20

65,086

10,122

1355

0.6

4,742,000,000

5

0.15

76,848

12,447

1584

0.5

5,630,000,000

Expected

51,749

8,208

1096

3,792,000,000

Value

DA – method comparison


Da results

Equipment

CPU

Unrouted

Scaled

Budget

Method

TEs

Rs

As

Cost

Seconds

Demand

Regret

Mean Value

51,800

8117

1081

3,780,000,000

0.7

15.5%

1.40

Stoch. Prog.

44,373

7446

918

3,273,000,00

0

1.8

20.4%

1.82

5,630,000,000

Worst Case

39,098

5495

757

2,773,000,000

4.6

27.2%

3.75

Robust Opt.

63,122

10,813

1425

4,734,000,000

2.7

5.2%

1.00

Mean Value

51,800

8117

1081

3,780,000,000

0.2

15.5%

1.11

Stoch. Prog.

44,373

7446

918

DA – results

3,273,000,000

0.6

20.4%

1.44

3,787,000,000

Worst Case

39,098

5495

757

2,773,000,000

2.1

27.2%

2.95

Robust Opt.

52,159

8108

1061

3,787,000,000

4.5

12.6%

1.00

No Feasible

Mean Value

0.3

100%

Solution

Stoch. Prog.

25,583

3696

515

1,832,000,000

3.9

42.3%

1.15

1

,848,000,000

Worst Case

27,180

2960

505

1,848,000,000

6.6

42.3%

1.51

Robust Opt.

25,856

3575

539

1,848,000,000

5.6

43.3%

1.00


Kl individual scenarios

KL – individual scenarios

Scenario

Prob.

TEs

Rs

As

CPU Seconds

Equipment Cost

1

0.15

12,767

7275

638

0.3

1,539,356,770

2

0.20

17,493

11,691

958

0.3

2,288,919,583

3

0.30

24,020

15,783

1178

0.3

3,052,619,167

4

0.20

29,295

19,196

1455

0.2

3,727,940,417

5

0.15

35,732

23,606

1760

0.3

4,554,614,375

Expected

23,837

15,545

1196

3,033,250,000

Value


Kl method comparison

CPU

Unrouted

Scaled

Budget

Method

TEs

Rs

As

Equip. Cost

Seconds

Demand

Regret

Mean Value

25,124

15,350

1221

3,094,700,000

1.1

15.4%

1.41

Stoch. Prog.

20,264

14,168

996

2,644,620,000

0.6

20.8%

1.94

4,554,610

,000

Worst Case

17,977

11,812

872

2,279,830,000

1.1

27.8%

4.05

Robust Opt.

27,520

21,348

1614

3,890,840,000

1.0

5.6%

1.00

Mean Value

23,978

15,382

1198

3,028,460,000

0.5

15.4%

1.11

Stoch. Prog.

KL – method comparison

20,264

14,168

996

2,644,620,000

0.2

20.8%

1.52

3,032,69

0,000

Worst Case

17,977

11,812

872

2,279,830,000

0.4

27.9%

3.20

Robust Opt.

23,967

15,548

1181

3,032,690,000

200.0

13.1%

1.00

No Feasible

Mean Value

?

100%

Solution

Stoch. Prog.

12,154

7456

666

1,537,150,000

2.7

42.7%

1.19

1,539,360,000

Wors

t Case

12,782

7222

645

1,539,360,000

1.9

44.4%

1.71

Robust Opt.

13,562

7172

575

1,539,360,000

5.6

43.3%

1.00


Network protection

Network Protection

  • Dedicated Protection – 1 + 1 Protection

  • P-Cycle Protection – Grover Stamatelakis(restoration speed of bi-directional rings at the cost of shared protection)

  • Shared Protection – Path Restoration

25


Robust designs for wdm routing and provisioning

Shared

Dedicated

No Protection

P-Cycle

3

3

3

3

2

2

2

2

6

6

6

6

4

4

4

4

5

5

5

5

1

1

1

1

TE = 2 A = 11 R = 5 Cost = 6.00

TE = 8 A = 32 R = 20 Cost = 18.00

TE = 6 A = 32 R = 15 Cost = 17.50

TE = 6 A = 32 R = 15 Cost = 17.5

Example 1 Demand: (1,4) of 192 DS3s (1 )

2

26


Robust designs for wdm routing and provisioning

Shared

Dedicated

No Protection

P-Cycle

3

3

3

3

2

2

2

2

6

6

6

6

4

4

4

4

5

5

5

5

1

1

1

1

TE = 18 A = 32 R = 45 Cost = 20.50

TE = 16 A = 32 R = 39 Cost = 19.92

TE = 18 A = 32 R = 43 Cost = 20.34

TE = 6 A = 20 R = 13 Cost = 11.34

Example 2 Demands: (1,4) 192 DS3s (1 ), (1,3) 384 DS3s ( 2 )

2

2

2

2

2

2

2

2

2

2

2

2

27


Robust designs for wdm routing and provisioning

Shared

No Protection

Dedicated

P-Cycle

3

3

3

3

2

2

2

2

6

6

6

6

4

4

4

4

5

5

5

5

1

1

1

1

TE = 70 A = 47 R = 181 Cost = 66.48

TE = 34 A = 45 R = 91 Cost = 31.48

TE = 70 A = 88 R = 185 Cost = 62.30

TE = 72 A = 88 R = 194 Cost = 63.12

Example 3Demands: (1,4) of 1 , (1,3) of 2 , (2,5) of 4 

4

4

2

4

2

2

4

2

4

2

2

4

4

2

4

4

4

2

2

2

4

6

2

2

4

2

2

6

2

2

4

28


Robust designs for wdm routing and provisioning

29


  • Login