Gaussian Belief Propagation for Solving Network Utility Maximization with Delivery Contracts

Gaussian Belief Propagation for Solving Network Utility Maximization with Delivery Contracts

Shengbin Liao and Jianyong Sun

Abstract

Classical network utility maximization (NUM) models fail to capture network dynamics, which are of increasing importance for modeling network behaviors. In this paper, we consider the NUM with delivery contracts, which are constraints to the classical model to describe network dynamics. This paper investigates a method to distributively solve the given problem. We first transform the problem into an equivalent model of linear equations by dual decomposition theory, and then use Gaussian belief propagation algorithm to solve the equivalent issue distributively. The proposed algorithm has faster convergence speed than the existing first-order methods and distributed Newton method. Experimental results have demonstrated the effectiveness of our proposed approach. View Full-Text

Keywords: network utility maximization; delivery contracts; Gaussian belief propagation; distributed algorithms

Full Paper can be downloaded at: https://www.mdpi.com/1099-4300/21/7/708

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