Distributed large scale network utility maximization

  • Authors:

  • Danny Bickson;Yoav Tock;Argyris Zyrnnis;Stephen P. Boyd;Danny Dolev

  • Affiliations:

  • IBM Haifa Research Lab, Haifa, Israel;IBM Haifa Research Lab, Haifa, Israel;Department of Electrical Engineering, Stanford University, Stanford, CA;Department of Electrical Engineering, Stanford University, Stanford, CA;School of Computer Science and Engineering, Hebrew University of Jerusalem, Jerusalem, Israel

  • Venue:
  • ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 2
  • Year:
  • 2009

Quantified Score

Hi-index 0.01

Visualization

Abstract

Recent work by Zymnis et al. proposes an efficient primal-dual interior-point method, using a truncated Newton method, for solving the network utility maximization (NUM) problem. This method has shown superior performance relative to the traditional dual-decomposition approach. Other recent work by Bickson et ale shows how to compute efficiently and distributively the Newton step, which is the main computational bottleneck of the Newton method, utilizing the Gaussian belief propagation algorithm. In the current work, we combine both approaches to create an efficient distributed algorithm for solving the NUM problem. Unlike the work of Zymnis, which uses a centralized approach, our new algorithm is easily distributed. Using an empirical evaluation we show that our new method outperforms previous approaches, including the truncated Newton method and dual-decomposition methods. As an additional contribution, this is the first work that evaluates the performance of the Gaussian belief propagation algorithm vs. the preconditioned conjugate gradient method, for a large scale problem.