Generating System Knowledge for Autonomic Control

Recently, autonomic computing researches have been applied based on the concepts of self-adaptation. Traditional autonomic control methods are used to model the process or to maximize the system resources in the external view of the managed system. However, they produce a heavy workload for the self-adaption engineers that must analyze the external environment without having access to internal information to model the managed system. To solve these problems, it is highly recommended to specify the managed object by generating the system’s knowledge. Thus, this paper describes an approach to implementing how to monitor system knowledge for autonomic control system. Through these activities, it is possible to minimize the efforts of analyzing the target system of a developer and reduce the system setting overloads for automatic setting of monitoring environment by obtaining the monitoring scope based on the internal system information. The approach is addressed in regard to defining system knowledge required for Autonomic Control from UML model.