### Abstract

This paper considers the optimization-based traffic allocation problem among multiple end points in connectionless networks. The network utility function is modeled as a non-concave function, since it is the best description of the quality of service perceived by users with inelastic applications, such as video and audio streaming. However, the resulting non-convex optimization problem, is challenging and requires new analysis and solution techniques. To overcome these challenges, we first propose a hierarchy of problems whose optimal value converges to the optimal value of the non-convex optimization problem as the number of moments tends to infinity. From this hierarchy of problems, we obtain a convex relaxation of the original non-convex optimization problem by considering truncated moment sequences. For solving the convex relaxation, we propose a fully distributed iterative algorithm, which enables each node to adjust its date allocation/rate adaption among any given set of next hops solely based on information from the neighboring nodes. Moreover, the proposed traffic allocation algorithm converges to the optimal value of the convex relaxation at a O(1/K) rate, where K is the iteration counter, with a bounded optimality. At the end of this paper, we perform numerical simulations to demonstrate the soundness of the developed algorithm.

Original language | English (US) |
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Title of host publication | 2017 American Control Conference, ACC 2017 |

Publisher | Institute of Electrical and Electronics Engineers Inc. |

Pages | 3980-3985 |

Number of pages | 6 |

ISBN (Electronic) | 9781509059928 |

DOIs | |

State | Published - Jun 29 2017 |

Event | 2017 American Control Conference, ACC 2017 - Seattle, United States Duration: May 24 2017 → May 26 2017 |

### Publication series

Name | Proceedings of the American Control Conference |
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ISSN (Print) | 0743-1619 |

### Other

Other | 2017 American Control Conference, ACC 2017 |
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Country | United States |

City | Seattle |

Period | 5/24/17 → 5/26/17 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Electrical and Electronic Engineering

### Cite this

*2017 American Control Conference, ACC 2017*(pp. 3980-3985). [7963565] (Proceedings of the American Control Conference). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ACC.2017.7963565

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*2017 American Control Conference, ACC 2017.*, 7963565, Proceedings of the American Control Conference, Institute of Electrical and Electronics Engineers Inc., pp. 3980-3985, 2017 American Control Conference, ACC 2017, Seattle, United States, 5/24/17. https://doi.org/10.23919/ACC.2017.7963565

**Non-concave network utility maximization in connectionless networks : A fully distributed traffic allocation algorithm.** / Wang, Jingyao; Ashour, Mahmoud; Lagoa, Constantino; Aybat, Necdet; Che, Hao; Duan, Zhisheng.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Non-concave network utility maximization in connectionless networks

T2 - A fully distributed traffic allocation algorithm

AU - Wang, Jingyao

AU - Ashour, Mahmoud

AU - Lagoa, Constantino

AU - Aybat, Necdet

AU - Che, Hao

AU - Duan, Zhisheng

PY - 2017/6/29

Y1 - 2017/6/29

N2 - This paper considers the optimization-based traffic allocation problem among multiple end points in connectionless networks. The network utility function is modeled as a non-concave function, since it is the best description of the quality of service perceived by users with inelastic applications, such as video and audio streaming. However, the resulting non-convex optimization problem, is challenging and requires new analysis and solution techniques. To overcome these challenges, we first propose a hierarchy of problems whose optimal value converges to the optimal value of the non-convex optimization problem as the number of moments tends to infinity. From this hierarchy of problems, we obtain a convex relaxation of the original non-convex optimization problem by considering truncated moment sequences. For solving the convex relaxation, we propose a fully distributed iterative algorithm, which enables each node to adjust its date allocation/rate adaption among any given set of next hops solely based on information from the neighboring nodes. Moreover, the proposed traffic allocation algorithm converges to the optimal value of the convex relaxation at a O(1/K) rate, where K is the iteration counter, with a bounded optimality. At the end of this paper, we perform numerical simulations to demonstrate the soundness of the developed algorithm.

AB - This paper considers the optimization-based traffic allocation problem among multiple end points in connectionless networks. The network utility function is modeled as a non-concave function, since it is the best description of the quality of service perceived by users with inelastic applications, such as video and audio streaming. However, the resulting non-convex optimization problem, is challenging and requires new analysis and solution techniques. To overcome these challenges, we first propose a hierarchy of problems whose optimal value converges to the optimal value of the non-convex optimization problem as the number of moments tends to infinity. From this hierarchy of problems, we obtain a convex relaxation of the original non-convex optimization problem by considering truncated moment sequences. For solving the convex relaxation, we propose a fully distributed iterative algorithm, which enables each node to adjust its date allocation/rate adaption among any given set of next hops solely based on information from the neighboring nodes. Moreover, the proposed traffic allocation algorithm converges to the optimal value of the convex relaxation at a O(1/K) rate, where K is the iteration counter, with a bounded optimality. At the end of this paper, we perform numerical simulations to demonstrate the soundness of the developed algorithm.

UR - http://www.scopus.com/inward/record.url?scp=85027024841&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85027024841&partnerID=8YFLogxK

U2 - 10.23919/ACC.2017.7963565

DO - 10.23919/ACC.2017.7963565

M3 - Conference contribution

AN - SCOPUS:85027024841

T3 - Proceedings of the American Control Conference

SP - 3980

EP - 3985

BT - 2017 American Control Conference, ACC 2017

PB - Institute of Electrical and Electronics Engineers Inc.

ER -