A data-driven solution for root cause analysis in cloud computing environments.

Die Zuverlässigkeit von Werkzeugmaschinen ist ein kritischer Faktor für den Erfolg produzierender Unternehmen. Durch die Analyse von Daten in der Produktplanung können Maschinenhersteller Ausfallursachen eliminieren und Maschinen systematisch verbessern. Jedoch stellt eine umfassende Datenanalyse viele Unternehmen vor große Herausforderungen. Die in diesem Beitrag vorgestellte Methodik adressiert diese Problematik und unterstützt Unternehmen bei der zielgerichteten Datenanalyse.   The reliability of machine tools is a critical factor for the success of manufacturing companies. By analyzing data in product planning, machine manufacturers can eliminate causes of failure and systematically improve machines. However, comprehensive data analysis poses great challenges for many companies. The methodology presented in this paper addresses this problem and supports companies in the goal-driven data analysis.

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Approximate QoS Rule Derivation Based on Root Cause Analysis for Cloud Computing

2019 IEEE 24th Pacific Rim International Symposium on Dependable Computing (PRDC) ◽

10.1109/prdc47002.2019.00020 ◽

2019 ◽

Author(s):

Satoshi Konno ◽

Xavier Defago

Keyword(s):

Cloud Computing ◽

Root Cause Analysis ◽

Cause Analysis ◽

Root Cause

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Identifying Most Relevant Performance Measures for Root Cause Analysis of Performance Degradation Events on a Private Cloud Computing Application: Experiment in an Industry Environment

British Journal of Mathematics & Computer Science ◽

10.9734/bjmcs/2016/27872 ◽

2016 ◽

Vol 18 (3) ◽

pp. 1-28

Author(s):

A Ravanello ◽

A April ◽

A Gherbi ◽

A Abran ◽

J Desharnais ◽

...

Keyword(s):

Cloud Computing ◽

Performance Measures ◽

Root Cause Analysis ◽

Performance Degradation ◽

Private Cloud ◽

Cause Analysis ◽

Root Cause ◽

Industry Environment ◽

Analysis Of Performance ◽

Private Cloud Computing

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Enterprise Tomography

Advancing Information Management through Semantic Web Concepts and Ontologies ◽

10.4018/978-1-4666-2494-8.ch007 ◽

2013 ◽

pp. 133-153

Author(s):

Jan Aalmink ◽

Timo von der Dovenmühle ◽

Jorge Marx Gómez

Keyword(s):

Cloud Computing ◽

Root Cause Analysis ◽

Semantic Integration ◽

Delta Operator ◽

Cause Analysis ◽

Time Consumption ◽

Root Cause ◽

Error Location ◽

Prerequisite Knowledge

Cloud Computing is finding its way into the architecture of current IT landscapes. The present chapter depicts an algorithm-based methodology supporting the Root-Cause-Analysis in the context of malfunctioning Federated ERP (FERP) software in Enterprise Clouds. The challenge is to standardize the error-finding procedure and increase the efficiency. For a given error symptom it is shown that the error location is approximated iteratively with help of generic operators in a semiautomatic manner. This approach of Semantic Debugging outperforms classical methods of Technical Debugging in efficiency regarding prerequisite knowledge and time consumption. Semantic integration and maintainability correlate strongly. The Delta-Operator enables the reconstruction of semantic FERP integration in the course of the error reproduction session. In combination with the Join-Operator, the defect approximation can be performed along the dependencies of semantic artifacts.

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