A Networked Software Optimization Mechanism Based on Gradient-Play

Networked software is a novel type of system deploying services on different devices and running based on the Internet. In order to improve service efficiency and realize a greater variety of functions, more software developers prefer to build systems in this way. However, the highly distributed characteristic brings obstacles to optimization of this kind of software. This paper is aimed at solving the optimization decision issues of networked software based on game theory. We let each software node exchange information with other nodes connecting to them and adjust their states for better payoffs, to achieve the purpose of improving overall system performance. In this process, we apply a consensus-based method to overcome the communication problems used to exist in the networked software system. With the method, each software node can make optimization decisions via incomplete system information. In addition, we propose an adaptive step size mechanism and a forced coordination mechanism to adjust parameters reasonably. These two mechanisms alleviate the problem of divergence and reduce the difficulty of parameter selection in this kind of methods, after that, an efficient synergy between state optimization and coordination of nodes can be realized. The experiments show that the original method can converge to Nash equilibrium more efficiently with these two mechanisms proposed by us.