This document discusses minimizing energy consumption in optical networks through improved routing techniques. It presents an integer linear programming model and heuristic algorithm to route demands across a network in a way that minimizes the number of links, nodes, and wavelengths used to reduce power consumption. The ILP model is solved using CPLEX and results are compared to a fast heuristic algorithm, showing the heuristic finds solutions with limited additional power consumption and runs much faster than the ILP model. Future work could test the approaches on larger network instances and topologies.

1. Green Optical Networks
with Signal Quality
Guarantee
João Rosa
Maria Stylianou
Zafar Gilani
CANO - Communication Networks Optimization
2012

2. Outline
● Introduction
● Problem description
● ILP model
● Heuristic
● Solution comparison
● Conclusions
● Possible future work

3. 1
Introduction
● Optimization is directly related to efficiency.
● Problem with power consumed by
communication networks.
○ Optical networks partially resolve the problem by
being better at consumption.
○ But need to consider improvements from other
related issues (such as efficient routing).

4. 2
Problem description
● Concern about rising energy consumption
and therefore costs of communication
networks.
● Energy efficient strategies are required for
network design provisioning that supports
both static and dynamic routing.

5. 3
Problem description
● In this project we try to minimize:
○ Number of links on a path.
○ Energy consumption of a path.
● We accomplish this by making improvements in
dynamic routing by consideration of:
○ Most economical links
○ Shortest path
○ Lowest power consumption
○ Reusing links or partial paths

6. 4
Environment Example
X1 OA OA OA X2
...
Tx Tx
X1,X2: Nodes
Tx: Transponder
OA OA
w1...wn OA: Optical Amplifier
w1...wn: Wavelengths

7. 5
Our Contribution
● ILP Model - CPLEX
● Heuristic Algorithm (Fasty)
● Comparison

8. 6
ILP model
Sets Variables
● N: Set of Nodes ● X[n]: 1 if node n is
● L: Set of Links used
● P: Set of Paths ● E[e]: 1 if link e is used
● W: Set of Wavelengths ● Xs[p,w]: 1 if
wavelength w for path
Constants p is used
● oe: #Optical Amplifiers (OA) ● y[e,w]: 1 if link e and
wavelength w is used
● eoa:Energy for 1 OA
● h[p]: # hops for each
● en: Energy for 1 node path p
● ew: Energy for 1 wavelength

9. 7
ILP model
● Objective function
Cumulative Cumulative Cumulative energy
energy of links energy of consumed by
used. nodes used. wavelengths used,
hops traversed and
nodes used over path
p for demand d.

10. 8
ILP model
For each demand, only one
● Constraints: wavelength can be used in
all paths

11. 9
ILP model
● Constraints:
A wavelength in a path
can be used only if the
same wavelength is
used in the link

12. 10
ILP model
● Constraints:
For each link e, ensure that
the number of wavelengths
used does not exceed the
maximum number of
wavelengths allowed

13. 11
ILP model
● Constraints:
Number of links used by a
node is less or equal to
number of links of a node

14. 12
Heuristic (Fasty)
● Own Implementation --> Works like a charm ;)
○ Code in C
○ Argument: same data file from CPLEX
● Goal: Satisfy all demands with the minimum power.
○ Minimum Power --> minimum links, nodes,
wavelengths used
● IDEA: Choose randomly a demand
○ Find all possible paths
○ Keep the path with the least power consumption
added

15. 13
Heuristic (Fasty)
Greedy Approach for choosing the "right" path
Demand #1 --> satisfied by 1-2-3-4 using λ1
Demand #2 --> satisfied by ?
λ1 λ2
1 2 1 2
λ1 λ1
λ1 λ1 λ2
λ1
4 3 4 3
λ1 λ2

16. 14
Solution comparison
● Execution time
● Optimal solution comparison
● Additional power consumption

17. 15
Execution time

18. 16
Optimal solution comparison
Limited
increase

19. 17
Additional power consumption
Heuristic with 8
demands.
No additional
power
consumption for
D5 after
satisfying D2.
Similar case for
D3, D0, D6 and
D4.

20. 18
Conclusions
● CPLEX is much slower than the Fasty
heuristic algorithm.
● Power increases as the demands increase
but only to a certain limit, as used links are
reused.
● For a given network graph, the heuristic
satisfies one demand after the other in such
a way as to reduce the cost in terms of
power consumed and path length.
○ Effective decrease in power used
○ .. with each new demand.

21. 19
Possible future work
● Test with larger tables/sets:
○ Demand-path set.
○ Path-link set.
● Test on multiple network graphs.
○ Different topologies.
○ Various routes.

22. Green Optical Networks
with Signal Quality
Guarantee
João Rosa
Maria Stylianou
Zafar Gilani
CANO - Communication Networks Optimization
2012