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  4. Network planning for stochastic traffic demands
 
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Network planning for stochastic traffic demands

Publikationstyp
Conference Paper
Date Issued
2013
Sprache
English
Author(s)
Tran, Phuong Nga  
Cahyanto, Bharata Dwi  
Timm-Giel, Andreas  orcid-logo
Institut
Kommunikationsnetze E-4  
TORE-URI
http://hdl.handle.net/11420/7003
Start Page
216
End Page
227
Citation
Mobile Networks and Management : 5th International Conference, MONAMI 2013, Cork, Ireland, September 23-25, 2013, revised selected papers / edited by Dirk Pesch, Andreas Timm-Giel, Ramón Agüero Calvo, Bernd-Ludwig Wenning, Kostas Pentikousis. - Cham : Springer, 2013. - (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST ; 125). - Seite 216-227
Contribution to Conference
5th International Conference Mobile Networks and Management, MONAMI 2013  
Publisher DOI
10.1007/978-3-319-04277-0_17
Scopus ID
2-s2.0-85009496048
Publisher
Springer
© Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2013. Traffic in communication networks is not constant but fluctuates heavily, which makes the network planning task very challenging. Overestimating the traffic volume results in an expensive solution, while underestimating it leads to a poor Quality of Service (QoS) in the network. In this paper, we propose a new approach to address the network planning problem under stochastic traffic demands. We first formulate the problem as a chance-constrained programming problem, in which the capacity constraints need to be satisfied in probabilistic sense. Since we do not assume a normal distribution for the traffic demands, the problem does not have deterministic equivalent and hence cannot be solved by the well-known techniques. A heuristic approach based on genetic algorithm is therefore proposed. The experiment results show that the proposed approach can significantly reduce the network costs compared to the peak-load-based approach, while still maintaining the robustness of the solution. This approach can be applied to different network types with different QoS requirements.
Subjects
Chance constrained programming
Genetic algorithm
Network planning
Stochastic traffic demands
DDC Class
620: Ingenieurwissenschaften
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