No 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.

Modern data storage systems have a sophisticated hardware and software architecture, including multiple storage processors, storage fabrics, network equipment and storage media and contain information, which can be damaged or lost because of hardware or software fault. Approach to storage software diagnostics, presented in current paper, combines a log mining algorithms for fault detection based on natural language processing text classification methods, and usage of the diagnostic model for a task of fault source detection. Currently existing approaches to computational systems diagnostics are either ignoring system or event log data, using only numeric monitoring parameters, or target only certain log types or use logs to create chains of the structured events. The main advantage of using natural language processing method for log text classification is that no information of log message structure or log message source, or log purpose is required if there is enough data for classificator model training. Developed diagnostic procedure has accuracy score comparable with existing methods and can target all presented in training set faults without prior log structure research.

The aim of the work is to develop a procedure for conducting an information security audit of the software system for predicting data storage failures in order to identify existing threats to information security, evaluate information security tools, and improve the efficiency of existing information security tools and introduce new ones. It is necessary to monitor the current situation to ensure information security in organizations where data storage systems are used. For this purpose, an audit system has been developed, including both organizational measures and software and hardware parts.

This paper describes application of diagnostic model, created with ontological modelling methods and machine learning text classifi cation algorithms, for fault detection, based on system log messages data, in enterprise-level storage system. Proposed fault detection model uses external procedures for the description ofthe relations between parameters and states of storage systems, based on the implementation of the machine learning algorithms. As an example of such relation, author describes application of the text classifi cation method for the task of software log analysis.