Bitte benutzen Sie diese Kennung, um auf die Ressource zu verweisen: http://dx.doi.org/10.18419/opus-9917
Autor(en): Pitakrat, Teerat
Titel: Architecture-aware online failure prediction for software systems
Erscheinungsdatum: 2018
Dokumentart: Dissertation
Seiten: X, 264
URI: http://elib.uni-stuttgart.de/handle/11682/9934
http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-99348
http://dx.doi.org/10.18419/opus-9917
Bemerkungen: Druck-Ausgabe bei Books on Demand, Norderstedt erschienen. ISBN 978-3-7528-7651-2
Zusammenfassung: Failures at runtime in complex software systems are inevitable because these systems usually contain a large number of components. Having all components working perfectly at the same time is, if at all possible, very difficult. Hardware components can fail and software components can still have hidden faults waiting to be triggered at runtime and cause the system to fail. Existing online failure prediction approaches predict failures by observing the errors or the symptoms that indicate looming problems. This observable data is used to create models that can predict whether the system will transition into a failing state. However, these models usually represent the whole system as a monolith without considering their internal components. This thesis proposes an architecture-aware online failure prediction approach, called Hora. The Hora approach improves online failure prediction by combining the results of failure prediction with the architectural knowledge about the system. The task of failure prediction is split into predictingthe failure of each individual component, in contrast to predicting the whole system failure. Suitable prediction techniques can be employed for different types of components. The architectural knowledge is used to deduce the dependencies between components which can reflect how a failure of one component can affect the others. The failure prediction and the component dependencies are combined into one model which employs Bayesian network theory to represent failure propagation. The combined model is continuously updated at runtime and makes predictions for individual components, as well as inferring their effects on other components and the whole system. The evaluation of component failure prediction is performed on three different experiments. The predictors are applied to predict component failures in a microservice-based application, critical events in Blue Gene/L supercomputer, and computer hard drive failures. The results show that the failures of individual components can be accurately predicted. The evaluation of the whole Hora approach is carried out on a microservice-based application. The results show that the Hora approach, which combines component failure prediction and architectural knowledge, can predict the component failures, their effects on other parts of the system, and the failures of the whole service. The Hora approach outperforms the monolithic approach that does not consider architectural knowledge and can improve the area under the Receiver Operating Characteristic (ROC) curve by 9.9%.
Enthalten in den Sammlungen:05 Fakultät Informatik, Elektrotechnik und Informationstechnik

Dateien zu dieser Ressource:
Datei Beschreibung GrößeFormat 
TeeratPitakrat-Dissertation.pdf5,1 MBAdobe PDFÖffnen/Anzeigen


Alle Ressourcen in diesem Repositorium sind urheberrechtlich geschützt.