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Detecting Operator Errors In Cloud Computing Using Anti-Patterns

Vetter, Arthur

IT services are subject of several maintenance operations like upgrades, reconfigurations or redeployments. Monitoring those changes is crucial to detect operator errors, which are a main source of service failures. Another challenge, which exacerbates operator errors is the increasing frequency of changes, e.g. because of continuous deployments. In this paper, we propose a monitoring approach to detect operator errors in real-time by using complex event processing and anti-patterns. The basis of the monitoring approach is a novel business process modelling method, combining TOSCA and Petri nets. This model is used to derive pattern instances, which are input for a complex event processing engine in order to analyze them against the generated events of the monitored applications.

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Verlagsausgabe §
DOI: 10.5445/IR/1000092977
Veröffentlicht am 27.05.2019
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2017
Sprache Englisch
Identifikator ISSN: 1613-0073
KITopen-ID: 1000092977
Erschienen in 7th International Symposium on Data-Driven Process Discovery and Analysis, SIMPDA 2017; Neuchatel; Switzerland; 6 December 2017 through 8 December 2017
Verlag RWTH Aachen
Seiten 68-82
Serie CEUR Workshop Proceedings ; 2016
Schlagwörter Complex Event Processing, Anti-Pattern, TOSCA, IT Service Management, Anomaly Detection.
Nachgewiesen in Scopus
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