Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms
International Journal of Network Management
Reducing power consumption in backbone networks
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Routing and scheduling for energy and delay minimization in the powerdown model
INFOCOM'10 Proceedings of the 29th conference on Information communications
Energy efficient network design tool for green IP/Ethernet networks
ONDM'10 Proceedings of the 14th conference on Optical network design and modeling
A genetic algorithm for shortest path routing problem and the sizing of populations
IEEE Transactions on Evolutionary Computation
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The rationale behind a green network is that it should effectively reduce energy consumption, while maintaining the level of services for data communications. In this paper, we propose an efficient approach, called the Compression Algorithm (CA), which is designed to solve the link on/off and weight assignment problems jointly so as to minimize a network's energy consumption. The problem is formulated as a mixed integer non-linear optimization problem. Because the problem is NP-hard, the CA utilizes a genetic algorithm to determine the link on/off schedule. In addition, it exploits the simulated annealing technique for link weight assignment so that the routing paths satisfy the link capacity constraints. By solving the link on/off and weight assignment problems sequentially, the CA scheme reduces the uncertainty about network energy consumption and yields a near optimal solution. To observe the relationship between network energy consumption and link load distributions, performance evaluations were conducted on three schemes, namely, the proposed CA, route construction without considering power savings, and route construction using minimum power saving without link capacity constraints. Numerical results demonstrate that the CA outperforms the other approaches on a network embedded with both uniform and non-uniform demand distributions.