TY - GEN

T1 - Non-concave network utility maximization

T2 - 2017 IEEE Conference on Computer Communications, INFOCOM 2017

AU - Ashour, Mahmoud

AU - Wang, Jingyao

AU - Lagoa, Constantino

AU - Aybat, Necdet

AU - Che, Hao

N1 - Funding Information:
This work was partially supported by NSF grants CNS-1329422, CMMI-1635106, FCC-1629625, and the China Scholarship Council.

PY - 2017/10/2

Y1 - 2017/10/2

N2 - This paper proposes an algorithm for optimal decentralized traffic engineering in communication networks. We aim at distributing the traffic among the available routes such that the network utility is maximized. In some practical applications, modeling network utility using non-concave functions is of particular interest, e.g., video streaming. Therefore, we tackle the problem of optimizing a generalized class of non-concave utility functions. The approach used to solve the resulting non-convex network utility maximization (NUM) problem relies on designing a sequence of convex relaxations whose solutions converge to that of the original problem. A distributed algorithm is proposed for the solution of the convex relaxation. Each user independently controls its traffic in a way that drives the overall network traffic allocation to an optimal operating point subject to network capacity constraints. All computations required by the algorithm are performed independently and locally at each user using local information and minimal communication overhead. The only non-local information needed is binary feedback from congested links. The robustness of the algorithm is demonstrated, where the traffic is shown to be automatically rerouted in case of a link failure or having new users joining the network. Numerical simulation results are presented to validate our findings.

AB - This paper proposes an algorithm for optimal decentralized traffic engineering in communication networks. We aim at distributing the traffic among the available routes such that the network utility is maximized. In some practical applications, modeling network utility using non-concave functions is of particular interest, e.g., video streaming. Therefore, we tackle the problem of optimizing a generalized class of non-concave utility functions. The approach used to solve the resulting non-convex network utility maximization (NUM) problem relies on designing a sequence of convex relaxations whose solutions converge to that of the original problem. A distributed algorithm is proposed for the solution of the convex relaxation. Each user independently controls its traffic in a way that drives the overall network traffic allocation to an optimal operating point subject to network capacity constraints. All computations required by the algorithm are performed independently and locally at each user using local information and minimal communication overhead. The only non-local information needed is binary feedback from congested links. The robustness of the algorithm is demonstrated, where the traffic is shown to be automatically rerouted in case of a link failure or having new users joining the network. Numerical simulation results are presented to validate our findings.

UR - http://www.scopus.com/inward/record.url?scp=85034039867&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85034039867&partnerID=8YFLogxK

U2 - 10.1109/INFOCOM.2017.8057155

DO - 10.1109/INFOCOM.2017.8057155

M3 - Conference contribution

AN - SCOPUS:85034039867

T3 - Proceedings - IEEE INFOCOM

BT - INFOCOM 2017 - IEEE Conference on Computer Communications

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 1 May 2017 through 4 May 2017

ER -