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[117424]
Title: Network Planning for Stochastic Traffic Demands. <em>Proceedings of the 5th International Conference on Mobile Networks and Management (MONAMI 2013)</em>
Written by: Phuong Nga Tran and Bahrata Cahyanto and Andreas Timm-Giel
in: sep (2013).
Volume: Number:
on pages:
Chapter:
Editor:
Publisher:
Series:
Address: Cork, Ireland
Edition:
ISBN:
how published:
Organization:
School:
Institution:
Type:
DOI:
URL: http://pollux.et6.tu-harburg.de/615/
ARXIVID:
PMID:

[www]

Note:

Abstract: 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.

[117424]
Title: Network Planning for Stochastic Traffic Demands. <em>Proceedings of the 5th International Conference on Mobile Networks and Management (MONAMI 2013)</em>
Written by: Phuong Nga Tran and Bahrata Cahyanto and Andreas Timm-Giel
in: sep (2013).
Volume: Number:
on pages:
Chapter:
Editor:
Publisher:
Series:
Address: Cork, Ireland
Edition:
ISBN:
how published:
Organization:
School:
Institution:
Type:
DOI:
URL: http://pollux.et6.tu-harburg.de/615/
ARXIVID:
PMID:

[www]

Note:

Abstract: 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.

[117424]
Title: Network Planning for Stochastic Traffic Demands. <em>Proceedings of the 5th International Conference on Mobile Networks and Management (MONAMI 2013)</em>
Written by: Phuong Nga Tran and Bahrata Cahyanto and Andreas Timm-Giel
in: sep (2013).
Volume: Number:
on pages:
Chapter:
Editor:
Publisher:
Series:
Address: Cork, Ireland
Edition:
ISBN:
how published:
Organization:
School:
Institution:
Type:
DOI:
URL: http://pollux.et6.tu-harburg.de/615/
ARXIVID:
PMID:

[www]

Note:

Abstract: 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.

[117424]
Title: Network Planning for Stochastic Traffic Demands. <em>Proceedings of the 5th International Conference on Mobile Networks and Management (MONAMI 2013)</em>
Written by: Phuong Nga Tran and Bahrata Cahyanto and Andreas Timm-Giel
in: sep (2013).
Volume: Number:
on pages:
Chapter:
Editor:
Publisher:
Series:
Address: Cork, Ireland
Edition:
ISBN:
how published:
Organization:
School:
Institution:
Type:
DOI:
URL: http://pollux.et6.tu-harburg.de/615/
ARXIVID:
PMID:

[www]

Note:

Abstract: 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.

[117424]
Title: Network Planning for Stochastic Traffic Demands. <em>Proceedings of the 5th International Conference on Mobile Networks and Management (MONAMI 2013)</em>
Written by: Phuong Nga Tran and Bahrata Cahyanto and Andreas Timm-Giel
in: sep (2013).
Volume: Number:
on pages:
Chapter:
Editor:
Publisher:
Series:
Address: Cork, Ireland
Edition:
ISBN:
how published:
Organization:
School:
Institution:
Type:
DOI:
URL: http://pollux.et6.tu-harburg.de/615/
ARXIVID:
PMID:

[www]

Note:

Abstract: 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.

[117424]
Title: Network Planning for Stochastic Traffic Demands. <em>Proceedings of the 5th International Conference on Mobile Networks and Management (MONAMI 2013)</em>
Written by: Phuong Nga Tran and Bahrata Cahyanto and Andreas Timm-Giel
in: sep (2013).
Volume: Number:
on pages:
Chapter:
Editor:
Publisher:
Series:
Address: Cork, Ireland
Edition:
ISBN:
how published:
Organization:
School:
Institution:
Type:
DOI:
URL: http://pollux.et6.tu-harburg.de/615/
ARXIVID:
PMID:

[www]

Note:

Abstract: 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.

[117424]
Title: Network Planning for Stochastic Traffic Demands. <em>Proceedings of the 5th International Conference on Mobile Networks and Management (MONAMI 2013)</em>
Written by: Phuong Nga Tran and Bahrata Cahyanto and Andreas Timm-Giel
in: sep (2013).
Volume: Number:
on pages:
Chapter:
Editor:
Publisher:
Series:
Address: Cork, Ireland
Edition:
ISBN:
how published:
Organization:
School:
Institution:
Type:
DOI:
URL: http://pollux.et6.tu-harburg.de/615/
ARXIVID:
PMID:

[www]

Note:

Abstract: 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.

TU Hamburg
Institute of Communication Networks (E-4)
Prof. Dr.-Ing. Andreas Timm-Giel
Am Schwarzenberg-Campus 3, Room E1.058
21073 Hamburg
E-mail : comnets@tuhh.de
Phone : +49 40 42878-3049/3249
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