A Service Self-Optimization Algorithm based on Autonomic Computing

Under the intrusion or abnormal attack, how to autonomously supply undegraded service to users is the ultimate goal of network securiy technology. Firstly, combined with martingale difference principle, a Service Self Optimization Algorithm based on Autonomic Computing-S2OAC is proposed. Secondly, according to the prior self optimizing knowledge and parameter information of inner environment, S2OAC searches the convergence trend of self optimizing function and executes the dynamic self optimization, aiming at minimum the optimization mode rate and maximum the service performance. Thirdly, set of the best optimization mode is updated and prediction model is renewed, which will implement the static self optimization and improve the accuricy of self optimization prediction. At last, the simulation results validate the efficiency and superiority of S2OAC.