Routing Optimization in IP/MPLS Networks under Per-Class Over-Provisioning Constraints

Communication Networks E. Mulyana, U. Killat

1

INOC 2005 – Lisbon – 22.03.2005

Routing Optimization in IP/MPLS Networks under Per-Class

Over-Provisioning Constraints

Eueung Mulyana, Ulrich Killat FSP 4-06 Communication Networks

Hamburg University of Technology (TUHH)


2

Hybrid Intra-Domain IP Routing

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 allows explicit (using ER-LSPs) other than shortest path routing (using Vanilla LSPs)

DiffServ gives possibility to differentiate treatements for IP packets with respect to their class of service e.g. class-based routing


3

Over-Provisioning (OP) (1)

Avoiding overload by ensuring that capacity of all links is greater than demand both in normal or in failure situations

large variations in traffic demand

QoS: the service that traffic receives is dependent upon the offered load and the available capacity

Simple capacity provisioning rules: upgrade when utilization reaches 40-50% over-provisioned by a factor of 2


4

Over-Provisioning (2)

VoIP 150 Mbps

best-effort(BE) 1.5 Gbps

Aggregate OP 2 lines of 2.5 Gbps

Per-class OP 1 line of 2.5 Gbps

Aggregate vs. per-class over-provisioning

2)Aggr(OP

c 03.3)Aggr(

4)VOIP(OP

c

2.1)BE(OP

c

67.16)VOIP(

57.1)BE(

t)(constrain valueOPgiven OP

c

valueOP actual

Per-class OP approach offers better service (guarantee) for prioritized VoIP class without large over-provisioning of capacity!


5

Over-Provisioning Factor

1

1

,,

*

,

s

s

jijiji lcc

jiji cc ,

1*

,

2*

, jic

1

, jil

2

, jil

jic ,

1

, jil

52

101

, ji

24

82

, ji

jic ,

1

, jil

2

, jil

52

101

, ji 67.142

102

,

ji

ji

ji

ji

l

c

,

*

,

,

1

,

,

,

s

s

ji

ji

ji

l

c

per-class cumulative


6

Problem Setting

Set of metric values (unique shortest path) for establishing vanilla Label Switched Paths (LSPs)

Set of Explicit Route (ER)-LSPs

Capacitated network

Traffic matrices of different classes

Per-class OP constraints

Per-class load distribution & per-class OP profile

Optimization

OP

,

*

,

),(min }{min

c

l

c

ji

ji

Aji

Actual minimum OP value

Given minimum OP constraint


7

Label Switched Path (LSP) Design (1) 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)


8

Label Switched Path Design (2)

optimize network(F)

optimize network(F1)



s

s

3

1

}3,2,1{

Weight System (WS)

base (WS0)

WS1

WS2

WS3

-

1

2

-

-

3

-

-

-

ji

ji

Aji c

l

,

,

),(max max ||

An example:

Objectives:

Minimizing and


9

Simulated Annealing Approach for Optimization Task (1)

Utilization Upperbound

Objective Function

} { min*

max

Al

lyc

*

max

*

, ji Aji ),(

Utilization

uv

uv

jiji ll )(,,

*

,

,*

,

ji

ji

ji

c

l Aji ),(

otherwise0

1 ww

y

o

ll

l

RTwwwwo

A

o

l

oo||21 ,,,,,

iAl RTwwww ||21 ,,,,,

Reference weight system:

A solution (current weight system):

LSPsER RT


10

Representation

Move Operator


2 1

3 4

5 6

w1

2 1 2 2 3 5 5

21 w

12 w

35 w

23 w

24 w

56 w

57 w

w2 w3 w4 w5 w6 w7

Simulated Annealing

w1

2 1 2 2 3 5 5

w2 w3 w4 w5 w6 w7 w1

2 1 5 2 3 5 5

w2 w3 w4 w5 w6 w7


11


Joint with plain local search (PLS):

concentrate the search around best solution (small ) first before exploiting other regions

speed-up convergence at small number of iterations

0

))()'(

exp(

1

T

xxp

)()'( xx

0 and )()'( PLS xx

otherwise

otherwise

satisfied are PLS performingfor conditions if

0

1

PLS


12

Case Study

3

13

9

14

11

8

10

6

5

74

2

1

12

2500 Mbps

net14 #nodes #links

14 nodes 44 links (directed)

effort)-(best 3

(assured) 2

(premium) 1

demands

interval mean

6.68 3

0.73 2

49.6 1

]556,0[ 3

]70,10[ 2

][10,150 1


13

Results: net14 (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


14

Results: net14 (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


15

Summary and Conclusion

Study of offline routing control in multi-class IP/MPLS networks:

using hybrid routing scheme

taking per-class OP constraints into account

Proposing a simple heuristic which iteratively calls a metric-based TE procedure, that minimizes per-class maximum utilization while minimizing the number of ER-LSPs

Starting from an optimized weight system for traffic aggregates, a few ER-LSPs are installed to improve minimum OP factors for each class


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Thank You !


17

References (Partial List) (1) Filsfils C., Evans J. „Engineering a Multiservice IP Backbone to

Support Tight SLAs“, Int. J. of Computers and Telecommunications Networking 40/1:131-148, 2002.

(2) Ben-Ameur W. et. al. „Routing Strategies for IP-Networks“, Telekronikk Magazine 2/3, 2001.

(3) Fortz B., Thorup M. „Internet Traffic Engineering by Optimizing OSPF Weights“, Proc. IEEE Infocom, 2000.

(4) Blake S. et al. „An Architecture for Differentiated Services“, RFC 2475, 1998.

(5) Le Faucher F. et al. „MPLS Support of Differentiated Services“, RFC 3270, 2002.

(6) Roberts J.W. „Traffic Theory and the Internet“, IEEE Communication Magazine, 2001.

(7) Smith P.A., Jamoussi B. „MPLS Tutorial and Operational Experiences“, NANOG 17 Meeting, 1999.


18

Calculating Link Load and Utilization

uv

uv

jiji ll )(,,

k

k

ji

uv

uv

jiji lll ,,, )(

jiji ll ,,

Link load for class

Link load for aggregate traffic

Using WS : Using WS0 :

only exist if (u,v) are not (head,tail) nodes of LSP in

Utilization

*

,

,*

,

ji

ji

ji

c

l

ji

ji

ji

c

l

,

,

,

ji

ji

ji

c

l

,

,

,

per-class utilization aggregate utilization effective per-class utilization


19

Results: net6

After optimize network(F) i.e. without ER-LSPs

OP for aggregate 1.26

After optimize network(F1) : 1 symmetrical ER-LSP

OP for aggregate 1.19

0.3OP

1 c

Education

Routing Optimization in IP/MPLS Networks under Per-Class Over-Provisioning Constraints