Veröffentlicht: 2004 August
Journal: IBM Systems Journal
Volume: Vol. 43
The goal of IBM's Autonomic Computing strategy is to deliver IT environments with improved self-management capabilities which cover the aspects of self-healing, self-protecting, self-optimizing, and self-configuring. Data correlation and inference technologies can be used as core components to build Autonomic Computing systems. They can be used to perform an automated, continuous analysis of enterprise-wide event data, based on user-defined, configurable rules, e.g. for detecting threats or system failures. Furthermore, they may trigger corrective actions for protecting or healing the system. In this paper, we discuss the use of ontologies as a high-level, expressive conceptual modelling approach for describing the knowledge on which the processing of a correlation engine is based upon. By introducing explicit models of state-based IT resources in the correlation technology approach, Autonomic Computing systems can be built which are able to deal with policy based goals on a higher abstraction level. We demonstrate some benefits of this approach by applying it to a particular IBM implementation, referred to as the "eAutomation" correlation engine.
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