NDLI: Error Diagnosis of Cloud Application Operation Using Bayesian Networks and Online Optimisation

Content Provider	IEEE Xplore Digital Library
Author	Xiwei Xu Liming Zhu Sun, D. An Binh Tran Weber, I. Min Fu Bass, L.
Copyright Year	2015
Description	Author affiliation: SSRG, NICTA, Sydney, NSW, Australia (Xiwei Xu; Liming Zhu; Sun, D.; An Binh Tran; Weber, I.; Min Fu; Bass, L.)
Abstract	Operations such as upgrade or redeployment are an important cause of system outages. Diagnosing such errors at runtime poses significant challenges. In this paper, we propose an error diagnosis approach using Bayesian Networks. Each node in the network captures the potential (root) causes of operational errors and its probability under different operational contexts. Once an operational error is detected, our diagnosis algorithm chooses a starting node, traverses the Bayesian Network and performs assertion checking associated with each node to confirm the error, retrieve further information and update the belief network. The next node in the network to check is selected through an online optimisation that minimises the overall availability risk considering diagnosis time and fault consequence. Our experiments show that the technique minimises the risk of faults significantly compared to other approaches in most cases. The diagnosis accuracy is high but also depends on the transient nature of a fault.
Starting Page	37
Ending Page	48
File Size	799299
Page Count	12
File Format	PDF
e-ISBN	9781467392891
DOI	10.1109/EDCC.2015.15
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-07
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Context Industries Runtime Systems operation Cloud Online Optimisation Bayes methods Diagnosis Operation Bayesian network Optimization Context modeling
Content Type	Text
Resource Type	Article

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