Efficient Planning and Offline Routing Approaches for IP Networks

Communication Networks Eueung Mulyana

1

Hamburg – 28.02.2006

Efficient Planning and Offline Routing Approaches for IP Networks

Eueung Mulyana

Hamburg University of Technology (TUHH)


2

Motivation IP Networks:

The important role of IP technology for the future communication networks

Current and Future Trends:

Heterogeneous environment

Routing Control:

Diverse applications with diverse Quality of Service (QoS) requirements

Avoiding congestion, increasing network efficiency, matching routing policies and preferences

Dynamics of IP networks:

Traffic variation, uncertainty


3

Previous Works

„Classical“ IP Networks:

Traffic engineering: Fortz(2000), Pioro(2001), ...

Routing and dimensioning: Bley(1998,2002), ...

Multi-Protocol Label Switching (MPLS):

LSP design: Haßlinger(2002), ...

Network dimensioning: Arvidsson(2002), ...

Demand Uncertainty:

Probabilistic assumption: Widjaja(2002), Mitra(2003), ...

Polyhedral model: Ben-Ameur(2003) ...


4

Contributions Classical IP Networks:

Metric based traffic engineering using hybrid genetic algorithms

Traffic engineering for transitional (IGP/MPLS) networks

Impact of demand increase on network conditions and re-optimization approaches

IP/MPLS:

Hybrid routing schemes using metrics and explicit routes

Routing and dimensioning for multi-class IP/MPLS networks with per-class over-provisioning requirements

Demand Uncertainty:

Several simple demand uncertainty models and the corresponding traffic engineering approaches


5

Outline

Overview of network planning, routing in the Internet and optimization approaches

Traffic engineering in classical and transitional IP networks

Routing and dimensioning of multi-class IP/MPLS networks

Routing under demand uncertainty

Summary and conclusion

Part :

1

2

3

4


6

Overview of Network Planning

1 2 3 4 Part :

Medium-Term Activities e.g. offline routing management

Short-Term Activities e.g. (near) real time traffic and resource management

Long-Term Activities e.g. network design

Forecast, Marketing Input (e.g. new customers)

Network

Traffic Data

Routing Update Various Controls

Traffic Data Topology, Capacity Change


7

Routing in the Internet (1)

1 2 3 4 Part :

server www.tuhh.de browser:

www.tuhh.de

Transport

Network

data streams

Transport

Network

data streams

packets

transport packets

Network

transport packets

packets

Routing


8

2

1

5

3

4

6

7

8

9

10

server www.tuhh.de

browser: www.tuhh.de

Routing in the Internet (2)

1 2 3 4 Part :

servers

2

5

servers

users

1 servers

users

servers

users

10

9

servers

users 8

servers

users 7 servers

users

servers

users

users 3

4

6

servers

users

users

servers


9

Optimization Approaches (1)

1 2 3 4 Part :

Linear programming

Stochastic approaches based on simple, greedy, meta-heuristics or a combination of them

Meta-Heuristics

Genetic Algorithms, Local Search

Hybridization

Simple Improving Heuristic

Search Algorithm

Solution

Improved Solution

Greedy Heuristic

Search Algorithm

Solution e.g. in terms of a sequence of demands

Objective Value


10

Optimization Approaches (2)

1 2 3 4 Part :

Linear programming

n

j

jj xcz1

i

n

j

jij bxa 1

Minimize

Subject to: ],1[ mi

Can be solved by the branch and bound or directly by the simplex algorithm (for cases without integer constraints)

Commercial solver CPLEX

Meta-Heuristics

Solution representation

Exploration strategies („move“ or „genetic“ operators)

Algorithms‘ specific parameters


11

Local Search (1)

A

B

C

D

E

A B

C

D

1 2 3 4 Part :

neighborhood of A

initial solution

move

Plain Local Search (PLS-1)

Search around temporary best solutions

Plain Local Search (PLS-2)

Search around a constant solution

neighborhood of B

neighborhood of C


12

Local Search (2)

1 2 3 4 Part :

A

B

C

D

E

F

1st neighborhood

of A

2nd neighborhood of A

3rd neighborhood of A

Variable neighborhood structure

solution space

neighborhood of x0

initial solution x0

best solution x*

End

temporary solution x

.

.

.

Simulated Annealing (SA)

SA allows moves towards less performing solutions


13

Genetic Algorithms

1 2 3 4 Part :

solution space

A

B

C

D

E

F

G

H

Initialize population

Exit condition fulfilled ?

Parents selection

Crossover Mutation

Remove some bad individuals Add new individuals

Survivors selection

END

yes no

individual

Iteration 1

Iteration 3 Iteration 2

Multi-agent (population-based)

Exploration using crossover and mutation operators


14

Routing in IP Networks: IGP

(b)(a)

6

11

1

1

1

1

2

21

2

3

5

5

121

3 4

5 6

2

3 4

5 6

1

2

4

6

5

3

1

2 3

4 5

1

Driven by link metrics (weights/costs)

Unique shortest path routing vs. Equal-Cost Multi-Path (ECMP)

ECMP e.g. [1-2-4-6] 50% [1-3-4-6] 25% [1-3-5-6] 25%

Unique shortest path routing: 1 unique path for all node pairs

2 1 3 4 Part :


15

Metric-Based Traffic Engieering

Utilization Upper bound

Objective Function

} { min max

max,

ji Aji ),(

Utilization

uv

vu

jiji ll,

,,

ji

ji

ji

c

l

,

,

, Aji ),(

2 1 3 4 Part :

Formulation


16

Transitional IP Networks (IGP/MPLS)

1

2 3

4 5

6 7

8 9

LSP

1

2 3

4 5

6 7

8 9

LSP

1

2 3

4 5

6 7

8 9

LSP

1 3

2 4

5 6

7 8

9

1

1 1

1 1

1

1

2

2

3

2

2

LSP

Basic IGP Shortcut (BIS) IGP Shortcut (IS) Overlay (OV)

2 1 3 4 Part :

}|| { minmax1

c

k

LSP

ji

uv

vu

jiji

klll,

,

,,

Objective Function Load


17

Network topology and link capacities

Traffic demand

Partial demand increase

Re-optimization

Analyze

Policy

not compliant

Weight Changes

Network Upgrade

Set of metric values

policy compliant

Partial Demand Increase

2 1 3 4 Part :

Mbps ]10,5[,

%2 vu

f%2

max Mbps ]50,5[,

%2 vu

f%2

max

Number of traffic- increase pattern


18

LSP Design and Weight Setting (1)

Vanilla LSP

ER LSP

2

1 2

3 5

2

5

1 2

3 4

5 6

Link Weights

1

2 3

4 5

6

1 2

3 4

5 6

MPLS+DiffServ

explicit routing (ER-LSPs), shortest path routing (Vanilla LSPs) or hybrid

Class-based routing

Per-class over-provisioning

3 2 1 4 Part :


19

LSP Design and Weight Setting (2) Indirectly solved by iteratively calling a metric-based traffic

engineering (TE) procedure using traffic matrices of different classes

F aggregate traffic matrix Fi traffic matrix for class i RT base routing pattern (obtained via optimization using F ) RTi routing pattern for class i (obtained via optimization using Fi)

3 2 1 4 Part :


20

Computational Study (1)

0.4OP

1 c

0.4OP

2 c After optimize network(F)

i.e. without ER-LSPs:

)1.1|4.3|3(min

%44.96max

3 2 1 4 Part :


21

Computational Study (2)

0.4OP

1 c

0.4OP

2 c

After optimize network(F2) : 13 symmetrical ER-LSPs (premium) and 4 symmetrical ER-LSPs (assured)

)1.1|01.4|05.4(min

%68.93max

3 2 1 4 Part :


22

Routing and Link Dimensioning

3 2 1 4 Part :

1

1

0

1

1

1

1

1 2

3 4

5 6

}2,1{

4OP

c

;20h

100k

1

2

e t

etty min

Objective Function

Capacity (with OP)

t

tet

d p i

idpdp

OP

edpkyxxc

1

1

e ,

Demand Satisfaction

p

dpu 1 d ,

dpddpuhx

pd ,,

Per-class routing & per-class over-provisioning (P1)

Single-path routing

Multi-path routing

realdp

u

binarydp

u


23

Backup Capacity

3 2 1 4 Part :

1 2

3 4 3

2 1

4

normal

backup

Demand (1,4) and (3,4) each

of 20 units

1

3

2

4

40

40

40 20

worst case load on each link

t

tetes

i

idpsidpdpskyzx

))(

1

1

se ,,

p

dpsdp

p

dpsdpsuv )1( sd ,,

ddsdpsdpshvz

spd ,,,

)((dpsdpdps

OP

d p

edpzxc

Demand Rerouting

Capacity

Failure Cases

1

3

2

4

20

20

20 0

normal case load on each link


24

Heuristic Approaches

3 2 1 4 Part :

Two-step strategy:

First consider only normal paths (ALG-1)

Heuristically assign a backup for each normal path


25

Computational Study

3 2 1 4 Part :

Problem (single-path

only)

P1

CPLEX

cost gap(%)

Greedy (best cost of 100 runs)

P2

P3

165.5 | 268.5

166.5 | 268.5

423.5 | 688

6.18 | 9.93

4.19 | 9.47

3.48 | 3.75

190.5 | 310.5

188.0 | 303.5

453.5 | 755

The best result from CPLEX is up to 15% (16%) better than the result from the heuristic

But, the heuristic (two-step strategy) is faster minutes vs. hours


26

.

.

.

Basic Outbound Model

1 20 -

2

3

4

5

6

100

100

100

100

100

100

1

2

3

4

5

6

}{\

out

,

uNv

uvuff

)(, vu

f )( out

uf

Specifying a traffic matrix

Specifying a vector of the maximum outbound traffic

Allowing traffic variations

The outbound model Without traffic uncertainty

1 2

20 20 20 20

3 4 5 6

- 20 20 20 20

- 20 20 20

- 20 20

- 20

-

20

20

20

20

20

20

20

20

20

20

20

20

20

20 20

20 - 50 5 5 5

- 20 20 20 20

- 20 20 20

- 10 1

- 20

-

20

20

60

20

20

20

20

20

20

2

20

20

20

20 20

20 - 20 20 20 20

- 0 99 0 1

- 20 20 20

- 20 20

- 5

-

0

20

20

0

20

20

20

0

20

20

0

20

95

20 20

4 2 3 1 Part :

uf out

u

. . . v

Outbound Model

Network


27

M1

Constraints Link load

(Upperbound)

M7

M3

M9

uvuf out

,

}{\

out

,

uNv

uvuff

urv

u

r

vuff out ,

,

u

r

vuf out ,

,

r v

vu

ji

u

r

vu

ji

v

u

r

u

ji

ur

ur

fl );max( min,

,out ,

,

,out ,,

Outbound Models

4 2 3 1 Part :

}{\

out

,

uNv

uvuff

);max( min}{\

,

,out

,

,}{\

out,

uNv

vu

ji

uvu

jiuNv

uu

ji fl

vu

jiuNv

uu

ji fl,

,}{\

out, max

vu

ji

,

,

Traffic fraction of flow (u,v) on link (i,j)

urv

u

r

vuff out ,

,

r

vu

ji

v

u

r

u

jiur

fl )max(,

,out ,,


28

Uncertainty Models : Summary

4 2 3 1 Part :

uf out

uf inu

. . . v

„Hose“ Model

Network

uf in

u

. . . v

Inbound Model

Network

M2

Model Model

Notation

outbound

inbound

M1

outbound + max_flow M3

inbound + max_flow M4

hose M5

hose + max_flow M6

M8

outbound + group

inbound + group

M7

outbound + max_flow + group M9

inbound + max_flow + group M10

hose + group M11

hose + max_flow + group M12


29

Computational Study

Uncertainty model M1 large number of traffic variations

A better solution for a certain model is not always better for the others

Upperbound (M1)

Traffic Matrix

Utilization

)(, tji

ji ,

t=1

t=100

4 2 3 1 Part :

Optimization based on M1

MSP Multiple Shortest Paths USP Unique Shortest Path


30

Partially Uncertain Demands

20 - 1

2

3

4

)(, vu

f

1 2

20 20

3 4

- 20 20

- 20

-

20

20

20

20

20 20

60

40

60

40

)( in

uf

1

2

3

4

60

40

60

40

)( out

uf

,maxmin()(,

,}{\

outunc,

u

vu

jiuNv

u

ji fl

uv

vu

ji

vu

ji fl,

,

,

fix, )(

unc,fix,, )()( jijiji lll

40

1 2

3 4

40

40

40

60

1 2

3 4

80

60

70

100

1 2

3 4

120

100

110

uncertain (hose)

fixed

partially uncertain

)max,

,}{\

in

u

uv

jiuNv

uf

4 2 3 1 Part :


31

Summary and Conclusion

Various efficient approaches for offline routing control and management in diverse IP networks, covering the classical IP networks as well as MPLS networks with and without service differentiation

Some novel mathematical formulations and heuristic frameworks, taking into account per-class over-provisioning requirements and different routing strategies

Our algorithms can find better routing solutions compared to those given by common routing configurations improving network efficiency

It is also possible to perform minimal routing reconfiguration in order to keep network performance within an acceptable range


32

Summary and Conclusion

Several simple demand uncertainty models whose impacts on network performance can easily be determined

The corresponding traffic engineering approach, including for the case where traffic is partially uncertain

Outlook

To address planning and traffic management problems in multi-layer networks e.g. IP over Optical networks

Mathematical programming approaches, that exploits the specific structure of the problem Branch-and-Cut, Branch-Cut-and-Price


33

Thank You !

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