Network Planning for Stochastic Traffic Demands
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.
KeywordsNetwork planning stochastic traffic demands chance constrained programming genetic algorithm
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