基于梯度博弈的网络化软件优化机制

为了提高服务效率和实现更多样的功能，越来越多的软件系统选择将业务或服务部署在不同的物理设备上，使用互联网通信协作，这类软件系统被称为网络化软件，然而此类软件高度分布的特点为系统的调控带来了难题.基于博弈理论解决网络化软件的优化决策问题，让系统中的软件节点交换信息，并根据收益函数调整自身状态，实现系统的整体优化；同时，通过多智能体一致性理论克服优化过程中可能存在通信的问题，让软件节点使用不完全的系统信息做出决策；此外，提出了自适应步长机制和强制协调机制，基于节点间的估计误差值对部分参数进行合理调整，有效缓解了此类方法容易发散、参数选择困难的问题，实现了状态寻优和估计误差修正间的有效协同，提高了方法的收敛速度.

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.