Hybrid prediction model for improving reliability in self-healing system

In ubiquitous environments, which involve an even greater number of computing devices, with more informal modes of operation, this type of problem will have rather serious consequences. In order to solve these problems when they arise, effective reliable systems are required. Also, system management is changing from a conventional central administration, to autonomic computing. However, most existing research focuses on healing after a problem has already occurred In order to solve this problem, a prediction model is required to recognize operating environments and predict error occurrence. In this paper, a hybrid prediction model through four algorithms supporting self-healing in autonomic computing, is proposed. This prediction model adopts a selective healing model, according to system situations for self-diagnosing and prediction of problems using four algorithms. In this paper, a hybrid prediction model is adopted to evaluate the proposed model in a self-healing system. In addition, prediction is compared with existing research and the effectiveness is demonstrated by experiment.