Self-Optimization Method of System Service Dependability Based on Autonomic Computing

According to the notion of autonomic computing,a network system service performance online real-time optimization method is proposed based on Q-learning algorithm. First, service performances are taken as parameters,which can affect network system as targets.Second,feed forward neural network is used as nonlinear mapping to gain executive action.Finally,the environment reward function values are calculated according to the change of system service performance and service availability.Then self learning feature and predictive ability of Q-learning is used to make the system service performance achieve optimization.The simulation results show that the optimization method has obvious advantages in credibility and service utility of the overall system maintenance.