Towards Automated Diagnosis of Application Faults using Wrapper Services and Machine Learning

Hofer, Juergen; Fahringer, Thomas

doi:10.1007/978-0-387-78446-5_21

Cited by 1 publication

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This paper presents follow-up work to a preceding workshop contribution [11]. The presented work was partially funded by the European Union through the IST FP6-004265 CoreGRID, IST FP6-031688 EGEE-2 and IST FP6-034601 Edutain@Grid projects.…”

Section: Acknowledgementsmentioning

confidence: 95%

Grid Application Fault Diagnosis Using Wrapper Services and Machine Learning

Hofer

Fahringer

2007

Service-Oriented Computing – ICSOC 2007

Self Cite

View full text Add to dashboard Cite

With increasing size and complexity of Grids manual diagnosis of individual application faults becomes impractical and timeconsuming. Quick and accurate identification of the root cause of failures is an important prerequisite for building reliable systems. We describe a pragmatic model-based technique for application-specific fault diagnosis based on indicators, symptoms and rules. Customized wrapper services then apply this knowledge to reason about root causes of failures. In addition to user-provided diagnosis models we show that given a set of past classified fault events it is possible to extract new models through learning that are able to diagnose new faults. We investigated and compared algorithms of supervised classification learning and cluster analysis. Our approach was implemented as part of the Otho Toolkit that 'serviceenables' legacy applications based on synthesis of wrapper service.

show abstract

Section: Acknowledgementsmentioning

confidence: 95%