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Gaussian Belief Propagation for Solving Network Utility Maximization with Delivery Contracts
Shengbin Liao1,2, Jianyong Sun3
1National Engineering Center for E-Learning, Huazhong Normal University, Wuhan 430079, China.
Entropy (Basel, Switzerland)
|December 3, 2020
Summary
This study introduces a new method for network utility maximization (NUM) using delivery contracts to model network dynamics. The approach uses Gaussian belief propagation for faster, distributed problem-solving.
Area of Science:
- Computer Science
- Network Engineering
Background:
- Classical network utility maximization (NUM) models do not account for network dynamics.
- Network dynamics are crucial for accurately modeling network behaviors.
Purpose of the Study:
- To develop a distributed method for solving network utility maximization with delivery contracts.
- To incorporate network dynamics into NUM models.
Main Methods:
- Transforming the problem into equivalent linear equations using dual decomposition.
- Applying the Gaussian belief propagation algorithm for distributed resolution.
Main Results:
- The proposed algorithm exhibits faster convergence compared to existing first-order and distributed Newton methods.
- Experimental results validate the effectiveness of the new approach.
Conclusions:
- The Gaussian belief propagation method offers an efficient and effective solution for distributed NUM with delivery contracts.
- This approach enhances the modeling of network dynamics in optimization problems.
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