Survivable Network Design

Traffic Grooming : The intelligent allocation of traffic demands onto an available set of wavelengths in a way that reduces the overall cost of the network. The Traffic Grooming Probl[r]

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Survivable Network Design

David Tipper

Associate Professor

Department of Information Science and

Telecommunications

University of Pittsburgh

Telcom

2110 Slides 15

Survivable Network Design

• Spare Capacity Allocation (SCA) Problem:

– given working paths and network (or virtual network) topology

– provision spare capacity and find backup routes for fault tolerance

– Goal: minimum

spare capacity or cost

• Survivable Mesh Networks

–

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Classification of

Survivability Techniques

• Path-based (Global) versus Link-based (Local)

• Failure Dependent vs Failure Independent

• Protection versus Restoration

• Dedicated-Backup versus Shared- Backup Capacity

• Ring versus Mesh topology

• Dual and multi-homing

•

P

cycle

• Etc.

Failure Dependent vs Failure Independent

• Failure Dependent – the backup path depends on which

device fails – need a set of paths one for each failure case

• Failure Independent – backup path link and node disjoint

with working path - one backup path per working path

• Example:

13

12

10

9

2

7

3

5

Working path

Failure Dependent backup path for link 1-2 failure

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SCA Problem

•

SCA for Failure Independent Shared Backup Path

Restoration

•

Notation

r = 1,2,…, D

set of demands (source-destination pairs)

p = 1,2,…, P

r

set of possible paths for demand pair r

l = 1,2,…, L

set of network links

•

Input parameters (constants)

α

r

offered traffic load of demand pair

r

c

l

unit cost of capacity on link

l

δ

l r,p

= 1

if

l

belongs to path

p

realizing demand

r

=

0,

otherwise

f

set of link failure scenarios

•

variables

x

r,p

flow of demand

r

on path

p

s

l

spare capacity on link

l

SCA Path-flow model

Find

s

l

and

x

r,p

, which

∑

⋅

L

l

s

c

minimize

D

r

x

r

P

p

r

=

∀

∈

∑

,

1

L

f

L

l

s

x

l

D

r p P

p r p r l r f r

∈

∀

−

∈

∀

≤

⋅

∑ ∑

,

},

{

,

δ

α

s.t.

Total spare capacity

Single backup path for each flow

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Matrix Based Formulation of SCA

•

Matrix Based formulation of Optimization model for FID

shared backup path restoration*

•

Consider

path incident matrices P

and

Q

for working and

backup paths where each matrix has

number of rows = number of flows in the network number of columns = number of links in the network – row iin the matrix Pcorresponds to the set of links used by flowi – where pij= 1if flow iuses link jit is 0otherwise

– similary row iin the matrix Qcorresponds to the set of backup path links used by flowi where qij= 1if flow iuses link jit is 0otherwise

•

Relate to spare provision matrix

G

, and spare capacity reservation

s

–

G

=

Q

P

, element G

ij

gives required spare capacity on link

i

when link

j

fails

–

s

= max(

G

), or

s

≥

G ,

spare capacity reservations are the maximum

spare capacity for any single link failure

• * Y.Liu, D.Tipper, and P Siripongwutikorn, “Approximating Optimal Spare Capacity Allocation by Successive Survivable Routing,'' ACM/IEEE Transactions on Networking, Vol 13., No 1, pp 198-211, Feb., 2005

Example

Link i 1 2 3 4 5 6 7

Backup path link incident matrix

1 2 1 1 0 1 1 1 0

2 2 1 0 1 0 1 0

3 1 0 1 0 0 0

4 1 1 0 1 0 1 0

5 1 0 1 0 0 0

6 0 1 0 0 0 1

7 2 0 0 1 0

11

Flows 3 45 10

src dst 0 0 0 a b 1 0 0 a c 1 0 0 3 a d 1 0 0 4 a e Working path link 0 0 0 b c incident matrix 0 1 0 b d 0 0 b e 0 0 0 c d

From

G

,

s=

max

G

From working and

backup paths,

G= Q

P

QT

G s

An example: when link fails,

3

4

5

a

c b

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Matrix Based SCA for Link Failures

min

S

=

e

s

Q,s

s.t.

s

≥

G

G = Q

M P

P + Q

≤

1

Q B

=

D

(mod 2)

Q

is a binary matrix

Decision variable:

Q, s

Given:

M

– traffic demand matrix

P

– working path link incidence matrix

B

and

D

– node-link & flow-node incidence matrices

Total spare capacity

Link-disjointed backup paths

Flow conservation of backup

Integer programming

Calculation of spare provision matrix

Enough spare capacity on each link

Another way to find

G

G =

Σ

r

G

r

, where

G

r

=

q

r

p

r

,

p

r

and

q

r

are

vectors for working and backup paths of flow

r

G2 G G1

GR GR-1

…

P

Q

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The Traffic Grooming Problem

• Number of wavelengths per fiber = -100+

• Per wavelength capacity = 2.5 Gbps to10 Gbps

• Bandwidth requirements of most applications << 2.5

Gbps

∴

Group several sessions on the same wavelength channel in

order to better utilize the available bandwidth

Traffic Grooming

:

The intelligent allocation of traffic

demands onto an available set of wavelengths in a way that

reduces the overall cost of the network.

The Traffic Grooming Problem:

CapEx

• Dominant cost factor: Electronic layer

multiplexing; number of electronic layer

Line Terminating Equipment (LTs):

– SONET/SDH ADMs

– IP/MPLS router ports

•

Solution

:

Assign the traffic such that

minimum number of LTs is used

3-4 times as

expensive as OXC

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Traffic Grooming Problems

• Network design problem:

dimensioning and

network provisioning

– Reduce capital and operational expenditure

– Maximize revenue

NP-Complete Problem

• Solution types:

– Exact solutions (based on ILP or MILP)

– Heuristic and approximate solutions

– Bounds

Traffic Grooming for Ring Networks: Heuristics

Heuristic Arbitrary

UPSR/BLSR Mustafa & Kamal ’03

SA Arbitrary

BLSR Wang et al ‘01

GA Arbitrary

UPSR Xu et al

Heuristic Arbitrary

UPSR/BLSR Zhang and Qiao ‘00

Heuristic Arbitrary

BLSR Wan et al ‘00

10/9 approximation Arbitrary

BLSR, single hub

Li et al ‘00

SA Uniform all-to-all Hubbed, and

single hop Cho et al ‘01

Heuristic Uniform all-to-all

BLSR Chiu and Modiano ’00

Heuristic Uniform all-to-all

BLSR Simmons et al ‘98

Result Traffic