Application of Ontology and Multivariate Decision Diagram in Cloud Monitor Systems

doi:10.23940/ijpe.19.12.p14.32273236

Abstract

Abstract: Cloud computing is different from distributed computing and grid computing and has its own characteristics. The existing cloud system is not sufficient in the unified identification of cloud resources and the dynamic joining management of new resources. According to the characteristics of cloud computing, this paper introduces the idea of ontology on cloud monitor system (CMS) based on bionic autonomic nervous system (BANS) and uses ontology web language (OWL) language to describe the resources of the system. It also establishes a reusable extended resource expression model. At the same time, the use of the third-party tool Jena for OWL semantic query also gives the monitoring system the characteristics of a rapid semantic query, which further enhances the convenience of cloud resource management. In addition, based on the application of ontology, we also introduce multivariate decision diagram (MDD) multi-valued decision graph technology, which allows B-CMS to self-diagnose complex system faults. The combination of ontology and MDD greatly simplifies the monitoring and management of large-scale systems, providing a fast and standardized means for the intelligent diagnosis of systems.

Key words: cloud computing, ontology, resource monitoring, fault detection, multivariate decision diagram, bionic autonomic nervous system

Han Xu, William Cheng Chung Chu, and Jie Luo. Application of Ontology and Multivariate Decision Diagram in Cloud Monitor Systems [J]. Int J Performability Eng, 2019, 15(12): 3227-3236.

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