Game Theory for Big Data Processing: Multileader Multifollower Game-Based ADMM

In this paper, tradeoff and convergence issues for incentive mechanisms are addressed by combining optimization and game theory. Specifically, a multiple-leader multiple-follower (MLMF) game-based alternating direction method of multipliers (ADMM) is developed that incentivizes the agents to perform a group of controllers' tasks in order to satisfy their corresponding objectives. Both analytical and simulation results verify that the proposed method reaches a hierarchical social optimum and converges linearly. More importantly, the convergence rate is independent of the network size, which indicates that the MLMF game-based ADMM can be used in very large networks, such as highly virtualized communication networks with two-layer hierarchies, for big data processing.