TY - GEN

T1 - Non-concave network utility maximization in connectionless networks

T2 - 2017 American Control Conference, ACC 2017

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.

Y2 - 24 May 2017 through 26 May 2017

ER -