Abstract
Monitoring and diagnosing the state of data storage systems, as well as assessing reliability and troubleshooting, require a formalized health model. A comparative analysis of existing knowledge representation methods has shown that an ontological approach is well suited for this task. This paper introduces a machine-represented data storage reliability ontology with an expert health model as baseline data. Classes of the ontology include the key terms of the reliability domain. Stated requirements for data interpretation tools allow further processing of the ontology-based knowledge base. Described ontology-based diagnostic systems have shown their applicability in the case of data storage systems in the construction industry.
Highlights
A data storage system integrates multiple hardware and software components to store, manage and protect user data
An adequate diagnostic model must satisfy the following requirements: - the failure modes must be representative of the system and its environment; - system states in the model must be mapped to the failure modes; - the levels of system performance degradation must be mapped to the defined system states; - any defined system state must be in one-to-one correspondence with a particular set of test parameters values
One of the ways to use the ontology is to develop algorithms for condition monitoring and diagnostics in a data storage system. As an example, such an algorithm would determine the current state of the system by searching for the closest match in a set of system states based on comparison of the monitoring data and the symptoms of the corresponding states, all described in the ontology
Summary
A data storage system integrates multiple hardware and software components to store, manage and protect user data. To prevent a system failure or diminish its consequences a data storage system should implement technical diagnostics as a part of a service path This diagnostic service should monitor the state of hardware and software components, analyze any change of health parameters values, detect occurring faults, promptly notify the operator of changes in the system’s performance and suggest recommendations for recovery. In this case a fault of CPU in storage controller puts the system into a vulnerable state and the loss of all controllers puts the system into a critical state This kind of expert health model would describe a set of known failure modes based on life data and user feedback. As a solution we present a formal approach to technical diagnosis of data storage systems using knowledge base methods
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