Maximum Bipartite Flow in Networks with Adaptive Channel Width

  • Authors:

  • Yossi Azar;Aleksander Mądry;Thomas Moscibroda;Debmalya Panigrahi;Aravind Srinivasan

  • Affiliations:

  • Microsoft Research, Redmond, WA 98052 and Tel Aviv University, Tel Aviv, Israel;Massachusetts Institute of Technology, Cambridge 02139;Microsoft Research, Redmond 98052;Massachusetts Institute of Technology, Cambridge 02139;Dept. of Computer Science and Institute for Advanced Computer Studies, University of Maryland, College Park 20742

  • Venue:
  • ICALP '09 Proceedings of the 36th Internatilonal Collogquium on Automata, Languages and Programming: Part II
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

Traditionally, combinatorial optimization problems (such as maximum flow, maximum matching, etc.) have been studied for networks where each link has a fixed capacity. Recent research in wireless networking has shown that it is possible to design networks where the capacity of the links can be changed adaptively to suit the needs of specific applications. In particular, one gets a choice of having few high capacity outgoing links or many low capacity ones at any node of the network. This motivates us to have a re-look at the traditional combinatorial optimization problems and design algorithms to solve them in this new framework. In particular, we consider the problem of maximum bipartite flow , which has been studied extensively in the traditional network model. One of the motivations for studying this problem arises from the need to maximize the throughput of an infrastructure wireless network comprising base-stations (one set of vertices in the bipartition) and clients (the other set of vertices in the bipartition). We show that this problem has a significantly different combinatorial structure in this new network model from the classical one. While there are several polynomial time algorithms solving the maximum bipartite flow problem in traditional networks, we show that the problem is NP-hard in the new model. In fact, our proof extends to showing that the problem is APX-hard. We complement our lower bound by giving two algorithms for solving the problem approximately. The first algorithm is deterministic and achieves an approximation factor of O (logN ), where there are N nodes in the network, while the second algorithm (which is our main contribution) is randomized and achieves an approximation factor of $\frac{e}{e-1}$.