, Graduate from Master in Distributed Computing & Intern at Telefonica
at UPC - Universitat Politecnica de Catalunya & KTH Royal Institute of Technology
Cano projectGreen Optical Networks with Signal Quality Guarantee
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Green Optical Networks with Signal Quality Guarantee João Rosa Maria Stylianou Zafar Gilani CANO - Communication Networks Optimization 2012
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Outline● Introduction● Problem description● ILP model● Heuristic● Solution comparison● Conclusions● Possible future work
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1Introduction● 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).
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2Problem 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.
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3Problem 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
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4Environment Example X1 OA OA OA X2 ... Tx Tx X1,X2: Nodes Tx: Transponder OA OA w1...wn OA: Optical Amplifier w1...wn: Wavelengths
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6ILP modelSets 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 pathConstants 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
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7ILP model● Objective functionCumulative Cumulative Cumulative energyenergy of links energy of consumed byused. nodes used. wavelengths used, hops traversed and nodes used over path p for demand d.
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8ILP model For each demand, only one● Constraints: wavelength can be used in all paths
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9ILP model● Constraints: A wavelength in a path can be used only if the same wavelength is used in the link
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10ILP model● Constraints: For each link e, ensure that the number of wavelengths used does not exceed the maximum number of wavelengths allowed
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11ILP model● Constraints: Number of links used by a node is less or equal to number of links of a node
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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
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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
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17Additional 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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18Conclusions● 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.
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19Possible future work● Test with larger tables/sets: ○ Demand-path set. ○ Path-link set.● Test on multiple network graphs. ○ Different topologies. ○ Various routes.
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Green Optical Networks with Signal Quality Guarantee João Rosa Maria Stylianou Zafar Gilani CANO - Communication Networks Optimization 2012
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