A Self-Optimization Mechanism of System Service Performance Based on Autonomic Computing

The security of computer and network is a key subject in computer field. Under the intrusion or abnormal attacks, how to supply service autonomously, without being degraded, to users is the ultimate goal of network securiy technology. People need an automated, flexible, fine-grain management method to solve the problem of security decline. Autonomic computing is regarded as a novel method to implement the security self-management of computer and network systems, which has been a frontier research hotspot with the character of subject cross in network security. Combined with the martingale difference principle, a self optimization mechanism based on autonomic computing—SOAC is proposed. According to the prior self optimizing knowledge and parameter information of inner environment, SOAC searches the convergence trend of self optimizing function and executes the dynamic self optimization, aiming at minimizing the optimization mode rate and maximizing the service performance. After that, the best optimization mode set is updated and a prediction model is constructed and renewed, which will implement the static self optimization and improve the accuracy of self optimization prediction. The two procedures interact and cooperate with each other, implementing the autonomic increase of system service performance in the changing inner environment. The simulation results validate the efficiency and superiority of SOAC.