This document summarizes a study on routing optimization in IP/MPLS networks under per-class over-provisioning constraints. It proposes a heuristic approach that iteratively calls a metric-based traffic engineering procedure to minimize per-class maximum utilization while minimizing the number of explicit route label switched paths (ER-LSPs). Starting from an optimized weight system for aggregate traffic, a few ER-LSPs are installed to improve minimum over-provisioning factors for each class. Simulation results on a 14-node network show this approach reduces the minimum over-provisioning factor compared to using only shortest path routing.

1. 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. Communication Networks
E. Mulyana, U. Killat
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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

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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. Communication Networks
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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)(constrainvalueOPgiven
OP
c
valueOPactual
 Per-class OP approach offers better service (guarantee) for
prioritized VoIP class without large over-provisioning of
capacity!

5. Communication Networks
E. Mulyana, U. Killat
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Over-Provisioning Factor
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per-class cumulative

6. Communication Networks
E. Mulyana, U. Killat
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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
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,
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min }{min 

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 c
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ji
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

Actual minimum
OP value
Given minimum
OP constraint

7. Communication Networks
E. Mulyana, U. Killat
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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. Communication Networks
E. Mulyana, U. Killat
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Label Switched Path Design (2)
optimize network(F)
optimize network(F1)
optimize network(F2)
optimize network(F3)
s
s


3
1
}3,2,1{
Weight System (WS)
base (WS0)
WS1
WS2
WS3
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 An example:
 Objectives:
Minimizing and

9. Communication Networks
E. Mulyana, U. Killat
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Simulated Annealing Approach for
Optimization Task (1)
Utilization Upperbound
Objective Function
}{min
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lyc
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Utilization
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Reference weight system:
A solution (current weight
system):
LSPsER RT

10. Communication Networks
E. Mulyana, U. Killat
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Representation
Move Operator
Simulated Annealing Approach for
Optimization Task (2)
21
3 4
5 6
w1
2 1 2 2 3 5 5
21 w
12 w
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Simulated Annealing
w1
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w2w3w4w5w6w7 w1
2 1 5 2 3 5 5
w2w3w4w5w6w7

11. Communication Networks
E. Mulyana, U. Killat
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Simulated Annealing Approach for
Optimization Task (3)
 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

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otherwise
otherwise
satisfiedarePLSperformingforconditionsif
0
1
PLS









12. Communication Networks
E. Mulyana, U. Killat
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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)-(best3
(assured)2
(premium)1






demands
interval mean
6.683
0.732
49.61






]556,0[3
]70,10[2
][10,1501







13. Communication Networks
E. Mulyana, U. Killat
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Results: net14 (1)
0.4
OP
1 c
0.4
OP
2 c
 After optimize network(F)
i.e. without ER-LSPs:
)1.1|4.3|3(min 


%44.96max 

14. Communication Networks
E. Mulyana, U. Killat
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Results: net14 (2)
0.4
OP
1 c
0.4
OP
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. Communication Networks
E. Mulyana, U. Killat
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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

16. Communication Networks
E. Mulyana, U. Killat
16
Thank You !

17. Communication Networks
E. Mulyana, U. Killat
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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. Communication Networks
E. Mulyana, U. Killat
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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



 *
,
,*
,
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ji
ji
c
l

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ji
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c
l
,
,
,


 
ji
ji
ji
c
l
,
,
, 
per-class utilization aggregate utilizationeffective per-class
utilization

19. Communication Networks
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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
0.3
OP
1 c