Archive of my talk at MMB & PGTS 2004 – Dresden – 14.09.2004

1. Communication Networks
E. Mulyana, U. Killat
1
MMB & PGTS 2004 – Dresden – 14.09.2004
Impact of Partial Demand Increase on
the Performance of IP Networks and
Re-optimization Approaches
Eueung Mulyana, Ulrich Killat
FSP 4-06 Communication Networks
Hamburg University of Technology (TUHH)

2. Communication Networks
E. Mulyana, U. Killat
2
Intra-Domain IP Routing : 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

3. Communication Networks
E. Mulyana, U. Killat
3
Motivation
 Partial demand changes :
 What happens ?
 performance parameter
 Need for re-optimization ?
 policy
 Re-optimization approaches :
 Partial (minimal) re-
configuration possible ?
 for ECMP-case [4]
 Which approaches ?
Multi-path routing
e.g. ECMP
Unique shortest
Path routing
Solution Space
(cf. [2])
(0,5] (5,10](10,20] (20,30] (30,50] (50,100] (100,200] (200,355]
0
5
10
15
20
25
30
Demand-Rate Distribution
NumberofDemands(%)
Rate Interval (Mbps)
Initial Distribution
10% Increase (5-10 Mbps)

4. Communication Networks
E. Mulyana, U. Killat
4
Problem Setting
Network topology
and link capacities
Traffic demand
Set of metric
values
Constraint(s)
Objective(s)
 Search for metric values that result in a unique shortest path routing
(ECMP is always enabled)
State of the
network (load
distribution,
etc.)Partial demand
increase
Analyze
Re-optimization
Performance
Check
Policy
Set of (changed)
metric values

5. Communication Networks
E. Mulyana, U. Killat
5
Utilization Upper bound
Objective Function
Formulation
}{min max
max,
 ji Aji  ),(
 Intended to Heuristics (it is not
suitable for Mathematical
Programming)
 Applicable for most core
networks
Utilization

uv
vu
jiji ll
,
,,
ji
ji
ji
c
l
,
,
,
 Aji  ),(

6. Communication Networks
E. Mulyana, U. Killat
6
1 2 3 4 5
1
2
3
4
5
Partial Demand Increase
Traffic Matrix
 FFF o 
Increase of Maximum Utilization
o
maxmaxmax 


Difference between maximum and
average utilization

  maxdiff
- 5 5 5 5
5 - 5 5 5
5 5 - 5 5
5 5 5 - 5
5 5 5 5 -
1 2 3 4 5
1
2
3
4
5
- 0 5 0 0
0 - 0 0 0
5 0 - 0 3
0 0 0 - 0
0 0 3 0 -
1 2 3 4 5
1
2
3
4
5
- 5 10 5 5
5 - 5 5 5
10 5 - 5 8
5 5 5 - 5
5 5 8 5 -
oF %20F %20F
oF %20F
Links Links
Util.
o
max
%20
max
%20

%20
diff
%20
max

7. Communication Networks
E. Mulyana, U. Killat
7
Network
instance
27 nodes (10 level-1 + 17 level-2);
76 directed-links (42 level-1 + 34 level-2)
Traffic
matrix
702 flows
Random in the interval [4,355]; Mean 34.6 Mbps
75% of the flows below 30 Mbps
Partial
increase
500 increase patterns (different );
Node pairs are chosen randomly: those sharing
the same level-1 node are excluded;
5 different ‘s
8 different increase intervals
Initial
parameters
(optimized
weights)
G-WiN Level 2G-WiN Level 1The G-WiN Network
Case Study
oF
F

%1.24
%4.39
%4.48
max
max






 level-1 network only
F

8. Communication Networks
E. Mulyana, U. Killat
8
Network Performance
Mbps]10,5[
,
%2 
vu
f Mbps]50,5[
,
%2 
vu
f
Mean UMax increase = 0.267 %
#increase patterns (Umax increase = 0%) = 38.6 %
#increase patterns (Umax increase > 0%) = 61.4 %
#increase patterns (Umax increase > 3%) = 0 %
Mean UMax increase = 1.4 %
#increase patterns (Umax increase = 0%) = 13.8 %
#increase patterns (Umax increase > 0%) = 86.2 %
#increase patterns (Umax increase > 3%) = 8.2 %
%2
max
%2
max
Number of traffic-increase pattern Number of traffic-increase pattern

9. Communication Networks
E. Mulyana, U. Killat
9
Impact of Demand Increase
2% 5% 10% 25% 50%
0
10
20
30
40
50
60
Increase of the UMax
Percentage of flows being increased
2% 5% 10% 25% 50%
0
10
20
30
40
50
60
Difference UMax - UAverage
Percentage of flows being increased
[2,5]
[5,10]
[10,20]
[20,30]
[20,30]
[2,5] [5,10]
[10,20]
The G-WiN Network; Increase bandwidth below 30 Mbps;
e.g. for =50% in the worst case this would result in
%33
%50
 diff
%29
%50
max 

 max

 diff
o
diff

10. Communication Networks
E. Mulyana, U. Killat
10
Re-optimization
21
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
w2w3w4w5w6w7
Symmetric case
2

 diff
1max 


Simple
Policy
Solution
Representation F
Links
o
max

 max



 diff

 max
and

11. Communication Networks
E. Mulyana, U. Killat
11
Re-optimization : PLS
Initial
solution x0
(Temporary)
best solutions x*
 Variable neighbourhood search around a constant starting solution
vector
 Exact control for (allowable) weight changes; Parts of the solution
space might be excluded (from exploration)
.
.
.
)( 01 xN
)( 02 xN
)( 03 xN

12. Communication Networks
E. Mulyana, U. Killat
12
Re-optimization : SA
Solution space
Neighbourhood
Initial
solution x0
Best
solution x*
End
Moves
.
.
.
Temporary
solution x
.
.
.
)( 0xN
 No exact control for (allowable) weight changes: search agent can
move everywhere in the solution space

13. Communication Networks
E. Mulyana, U. Killat
13
Re-optimization : Results
(%)
1
(%)
2
15
15
20
20
25
30
25
30
a(%)
b(%) c(%)
PLS SA
Mbps]100,5[
,
%10 
vu
f
25.8
23.6
24.4
2.6
48.84
54.24
53.28
92.31
3.63
3
3.2
3.29
b(%) c(%)
25.58
31.36
37.7
84.62
24.16
20.63
20.25
20.57
a
Percentage of different increase
patterns, which trigger the
re-optimization procedure
b
Percentage of successful
re-optimization
c
Average value of weight
changes yielded by all
successful re-optimizations
Termination : 500 iterations (maximum) or 300 iterations (no improvements)
Interval for metric values : 1  wk  80
Changes upper-bound for PLS : 13 % (5 symmetrical links)
A re-optimization is successful when: and 2

 diff1max 



14. Communication Networks
E. Mulyana, U. Killat
14
Summary and Conclusion
 Investigation of the impact of partial demand increase on the network
performance
 Proposing a simple policy for metric-based TE re-optimization
 Re-optimization approaches based on local search (PLS; SA) :
 which are applied to unique shortest path routing scheme
 which take „re-configuration costs(changes)“ into account
 Starting with an optimized set of metric values, PLS peforms better
than SA both in terms of the number of succesful re-optimizations
and the number of weight changes
 For a given network and a set of metric values, it is possible to
predict network performance influenced by a certain inaccuracy in the
traffic matrix

15. Communication Networks
E. Mulyana, U. Killat
15
References (Partial List)
(1) Awduche D. et. al. „Overview and Principles of Internet Traffic
Engineering“, RFC 3272, May 2002.
(2) Ben-Ameur W. et. al. „Routing Strategies for IP-Networks“,
Telekronikk Magazine 2/3, 2001.
(3) Bley A., Koch T. „Integer Programming Approaches to Access and
Backbone IP Network Planning“, Preprint ZIB ZR-02-41, 2002.
(4) Forzt B., Thorup M. „Optimizing OSPF/IS-IS Weights in a Changing
World“, IEEE JSAC, 20(4):756-767, 2002.
(5) Karas P., Pioro M. „Optimisation Problems Related to the
Assignment of Administrative Weights in the IP Networks‘ Routing
Protocols“, Proceedings of 1st PGTS 2000.
(6) Staehle D. et. al. „Optimization of IP Routing by Link Cost
Specification“, Tech. Report, University of Wuerzburg, 2000.
(7) Thorup M., Roughan M. „Avoiding Ties in Shortest Path First
Routing“,[online].

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

17. Communication Networks
E. Mulyana, U. Killat
17
Impact of Demand Increase
(cnt‘d)
2% 5% 10% 25% 50%
0
10
20
30
40
50
60
Increase of the UMax
Percentage of flows being increased
2% 5% 10% 25% 50%
0
10
20
30
40
50
60
Difference UMax - UAverage
Percentage of flows being increased
[50,50]
[100,100]
[5,100]
[50,50]
[5,50]
[5,100]
[5,50]
[100,100]

 max

 diff
o
diff

18. Communication Networks
E. Mulyana, U. Killat
18
Convergence (SA)
objValue (bestAgent) = 514.218
utilMaxCons (b.A.) = 0.513666
weightChanges (b.A.) = 55.2632 %
----------------------------
Total Computation Time = 2.5 minutes
Computation Time (Network) = 2.48333 minutes
Computation Time (Network) = 99.3333 %
Temperature
Current Agent
Best Agent
Iterations
Objective
value

19. Communication Networks
E. Mulyana, U. Killat
19
Convergence (PLS)
objValue (bestAgent) = 703.185
utilMaxCons (b.A.) = 0.702974
weightChanges (b.A.) = 21.0526 %
----------------------------
Total Computation Time = 2.7 minutes
Computation Time (Network) = 2.7 minutes
Computation Time (Network) = 100 %
objValue (bestAgent) = 982.071
utilMaxCons (b.A.) = 0.981913
weightChanges (b.A.) = 15.7895 %
----------------------------
Total Computation Time = 3.66667 minutes
Computation Time (Network) = 3.66667 minutes
Computation Time (Network) = 100 %
Reference/predefined
Agent
Best Agent
Best Agent
Iterations
Objective
value
Objective
value

20. Communication Networks
E. Mulyana, U. Killat
20
Objective Function
Guided Move
w1
o, w2
o, … , wk
o, … , w|A|
o
w1 , w2 , … , wk , … , w|A| 
ref
kw
o
kk
o
kk wwww 
ref
kw
}
1
c{min max 


Ak
ky
|A|

o
kk
o
kk wwww 





 o
kk
o
kk
k
ww
ww
y
0
1

21. Communication Networks
E. Mulyana, U. Killat
21
Applicability for Using max
 Should be ensured that it is always possible to reroute load in each
link in the network
 If not the case : transform
.
maxmax
cons
 
2
3
4 5
6
7
1
considered
not
considered
Mark each link as
considered or
unconsidered

22. Communication Networks
E. Mulyana, U. Killat
22
|Rall|  cardinality of all routing entries for all nodes
|D|  cardinality of all demand entries
c (>>)  a quite high constant
Objective function
Unique Shortest-Path Routing :
Heuristics
 Thorup, Roughan „Avoiding Ties in Shortest Path First Routing“
 Using an explicit penalty in the objective function :
2
3
41
vu
f ,
2
,vu
f
2
,vu
f
2
3
41



c
fcf vuvu ,,
2
,

vu
f
2
,

vu
f
|)||(| all
DRc 

