Autonomic Dependability Evaluation Research Of Automic Computing System

With the rapid development and wide applications of computer technology, and highly integration of software and hardware devices, traditional technologies cannot meet the need of the reliability and security any longer. New theories and methods are expected , which Autonomic Computing(AC)theory is proposed to resolve the issues of computer management and safety crisis,which the frequencies and complexities of manually intervention are reduced. Then the reliability and security of system is improved. However, AC research is still in infancy, many crucial technologies have not been solved, especially in autonomic maturity research of AC system evaluation. Evolution process of AC is limited.At present, there are few criterion and measurable methods to analyze quantitatively autonomicity of AC system.In this dissertation, combining dependability with autonomicity, the work is mainly focused on the autonomic-dependability indexes and modeling of system service based on service-oriented. The research aims to solve issues of qualitative description and analysis of AC service evaluation, and the main research contents are organized as follows:First, a multi-agent analysis model of AC evaluation based on service-oriented is established. States and behaviors of system are described by Web service and XML technology to modeling.Service-oriented idea focuses only on the service characteristic of AC system instead of special technologies. A general standard of AC system evaluation is set up. Experiment results indicate that it can accurately reflect service key properties and autonomic characters of AC system. The multi-agent model provides the guiding in model and quantitative analysis of AC system.Second, according to the relationship between autonomic unit and service of AC system evaluation model, autonomic-dependability is quantitative analyzed from three respects of service requests, service processes and service responses, comprehensive indexes which has an important impact to autonomic services are extracted. Then a hierarchical evaluation system for autonomic-dependability is established. Application in specific case indicates that the research can be used to analysis and evaluation in different AC systems. The method provides a reasonable basis for autonomic-dependability analysis.Third, HMM autonomic-dependability evaluation method based clustering analysis is proposed. Autonmoic behaviors and service states are extracted into HMM. Effective autonoimc states are extracted from system states by clustering algorithm, in order to improve model ability to the recognition of target system.Then, information entropy is applied to optimize the model parameter selection which improves model accuracy. Experiment results show that the model can accurately reflect the logic relation and transformation for autonomic-dependability elements. It is an important basis for quantitative analysis.Finally, a forecasting method for system autonomic-dependability based on Support Vector Machine (SVM) is proposed. The autonomic-dependability is forecasted by regarding the system service data as SVM input and the service level probability as output. The method overcomes drawbacks of the origin evaluation data such as nonlinearity, fuzzy, uncertainty and discreteness. Quantitative analysis for autonomic-dependability is realized. Experiment results show that has a higher forecasting precision. The research provides to optimize the AC system design.