Green Optical Networks with Signal Quality

Guarantee

João RosaMaria Stylianou

Zafar Gilani

CANO - Communication Networks Optimization2012

Outline

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

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).

1

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.

2

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

3

Environment Example

X1,X2: NodesTx: TransponderOA: Optical Amplifierw1...wn: Wavelengths

X1 X2

Tx Tx

OA OA OA

OA OAw1...wn

4

...

Our Contribution

● ILP Model - CPLEX ● Heuristic Algorithm (Fasty) ● Comparison

5

Sets● N: Set of Nodes● L: Set of Links● P: Set of Paths● W: Set of Wavelengths

ILP model

Constants● oe: #Optical Amplifiers (OA)● eoa:Energy for 1 OA● en: Energy for 1 node● ew: Energy for 1 wavelength

Variables● X[n]: 1 if node n is

used ● E[e]: 1 if link e is used● Xs[p,w]: 1 if

wavelength w for path p is used

● y[e,w]: 1 if link e and wavelength w is used

● h[p]: # hops for each path p

6

ILP model

● Objective function

Cumulative energy of links used.

Cumulative energy of nodes used.

Cumulative energy consumed by wavelengths used, hops traversed and nodes used over path p for demand d.

7

ILP model

● Constraints:

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For each demand, only one wavelength can be used in all paths

ILP model

● Constraints: A wavelength in a path

can be used only if the same wavelength is used in the link

9

ILP model

● Constraints:

For each link e, ensure that the number of wavelengths used does not exceed the maximum number of wavelengths allowed

10

ILP model

● Constraints:

Number of links used by a node is less or equal to number of links of a node

11

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

12

Heuristic (Fasty)Greedy Approach for choosing the "right" path

Demand #1 --> satisfied by 1-2-3-4 using λ1Demand #2 --> satisfied by ?

1 2

4 3

1 2

4 3

λ1

λ2

λ2

λ2

λ1

λ1

λ1λ1 λ1

λ1

13

Solution comparison

● Execution time ● Optimal solution comparison ● Additional power consumption

14

Execution time

15

Optimal solution comparison

16

Limited increase

Additional power consumption

Heuristic with 8 demands.

No additional power consumption for D5 after satisfying D2. Similar case for D3, D0, D6 and D4.

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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.

18

Possible future work

● Test with larger tables/sets:○ Demand-path set.○ Path-link set.

● Test on multiple network graphs.○ Different topologies.○ Various routes.

19

Green Optical Networks with Signal Quality

Guarantee

João RosaMaria Stylianou

Zafar Gilani

CANO - Communication Networks Optimization2012