Simultaneous routing and resource allocation via dual decomposition

In wireless data networks, the optimal routing of data depends on the link capacities which, in turn, are determined by the allocation of communications resources (such as transmit powers and bandwidths) to the links. The optimal performance of the network can only be achieved by simultaneous optimization of routing and resource allocation. In this paper, we formulate the simultaneous routing and resource allocation (SRRA) problem, and exploit problem structure to derive efficient solution methods. We use a capacitated multicommodity flow model to describe the data flows in the network. We assume that the capacity of a wireless link is a concave and increasing function of the communications resources allocated to the link, and the communications resources for groups of links are limited. These assumptions allow us to formulate the SRRA problem as a convex optimization problem over the network flow variables and the communications variables. These two sets of variables are coupled only through the link capacity constraints. We exploit this separable structure by dual decomposition. The resulting solution method attains the optimal coordination of data routing in the network layer and resource allocation in the radio control layer via pricing on the link capacities.

[1]  Panganamala Ramana Kumar,et al.  A network information theory for wireless communication: scaling laws and optimal operation , 2004, IEEE Transactions on Information Theory.

[2]  Jean-Philippe Vial,et al.  Convex nondifferentiable optimization: A survey focused on the analytic center cutting plane method , 2002, Optim. Methods Softw..

[3]  Steven H. Low,et al.  Optimization flow control—I: basic algorithm and convergence , 1999, TNET.

[4]  Bezalel Gavish,et al.  A system for routing and capacity assignment in computer communication networks , 1989, IEEE Trans. Commun..

[5]  Frank Kelly,et al.  Rate control for communication networks: shadow prices, proportional fairness and stability , 1998, J. Oper. Res. Soc..

[6]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[7]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[8]  John N. Tsitsiklis,et al.  Parallel and distributed computation , 1989 .

[9]  Dimitri P. Bertsekas,et al.  Network optimization : continuous and discrete models , 1998 .

[10]  Anthony Ephremides,et al.  Information Theory and Communication Networks: An Unconsummated Union , 1998, IEEE Trans. Inf. Theory.

[11]  Vivek S. Borkar,et al.  Distributed Asynchronous Incremental Subgradient Methods , 2001 .

[12]  Dimitri P. Bertsekas,et al.  Nonlinear Programming , 1997 .

[13]  Elizabeth M. Belding-Royer,et al.  A review of current routing protocols for ad hoc mobile wireless networks , 1999, IEEE Wirel. Commun..

[14]  David Tse,et al.  Optimal power allocation over parallel Gaussian broadcast channels , 1997, Proceedings of IEEE International Symposium on Information Theory.

[15]  Stephen P. Boyd,et al.  Optimal power control in interference-limited fading wireless channels with outage-probability specifications , 2002, IEEE Trans. Wirel. Commun..

[16]  Yinyu Ye,et al.  Interior point algorithms: theory and analysis , 1997 .

[17]  Yurii Nesterov,et al.  Interior-point polynomial algorithms in convex programming , 1994, Siam studies in applied mathematics.

[18]  Cem U. Saraydar,et al.  Efficient power control via pricing in wireless data networks , 2002, IEEE Trans. Commun..

[19]  Robert G. Gallager,et al.  A Minimum Delay Routing Algorithm Using Distributed Computation , 1977, IEEE Trans. Commun..

[20]  David Tse,et al.  Multiaccess Fading Channels-Part I: Polymatroid Structure, Optimal Resource Allocation and Throughput Capacities , 1998, IEEE Trans. Inf. Theory.

[21]  Dimitri P. Bertsekas,et al.  Data Networks , 1986 .

[22]  R. Tyrrell Rockafellar,et al.  Augmented Lagrangians and Applications of the Proximal Point Algorithm in Convex Programming , 1976, Math. Oper. Res..

[23]  Mikael Prytz On Optimization in Design of Telecommunications Networks with Multicast and Unicast Traffic , 2002 .

[24]  Lin Xiao,et al.  Cross-layer optimization of wireless networks using nonlinear column generation , 2006, IEEE Transactions on Wireless Communications.

[25]  T. Stern,et al.  A Class of Decentralized Routing Algorithms Using Relaxation , 1977, IEEE Trans. Commun..

[26]  K. Kiwiel Approximations in proximal bundle methods and decomposition of convex programs , 1995 .

[27]  Bruce E. Hajek,et al.  Link scheduling in polynomial time , 1988, IEEE Trans. Inf. Theory.

[28]  Philippe Mahey,et al.  A Survey of Algorithms for Convex Multicommodity Flow Problems , 2000 .

[29]  Jens Zander,et al.  Radio resource management in future wireless networks: requirements and limitations , 1997, IEEE Commun. Mag..

[30]  Leonard Kleinrock,et al.  Communication Nets: Stochastic Message Flow and Delay , 1964 .

[31]  Leandros Tassiulas,et al.  Jointly optimal routing and scheduling in packet radio networks , 1992, IEEE Trans. Inf. Theory.

[32]  Fernando Paganini,et al.  Internet congestion control , 2002 .

[33]  Naum Zuselevich Shor,et al.  Minimization Methods for Non-Differentiable Functions , 1985, Springer Series in Computational Mathematics.

[34]  Hong-Hsu Yen,et al.  Near-optimal delay constrained routing in virtual circuit networks , 2001, Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No.01CH37213).

[35]  Stephen P. Boyd,et al.  Simultaneous routing and power allocation in CDMA wireless data networks , 2003, IEEE International Conference on Communications, 2003. ICC '03..

[36]  Andrea J. Goldsmith,et al.  Capacity and optimal resource allocation for fading broadcast channels - Part I: Ergodic capacity , 2001, IEEE Trans. Inf. Theory.