Localizing Runtime Anomalies in Service-Oriented Systems

He, Q; Xie, X; Wang, Y; Ye, D; Chen, F; Jin, H; Yang, Y

Localizing Runtime Anomalies in Service-Oriented Systems

He, Q Xie, X Wang, Y Ye, D

Chen, F Jin, H Yang, Y

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Transactions on Services Computing, 2017, 10, (1), pp. 94-106
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	He, Q
dc.contributor.author	Xie, X
dc.contributor.author	Wang, Y
dc.contributor.author	Ye, D https://orcid.org/0000-0002-7561-0992
dc.contributor.author	Chen, F
dc.contributor.author	Jin, H
dc.contributor.author	Yang, Y
dc.date.accessioned	2022-08-17T04:20:59Z
dc.date.available	2022-08-17T04:20:59Z
dc.date.issued	2017-01-01
dc.identifier.citation	IEEE Transactions on Services Computing, 2017, 10, (1), pp. 94-106
dc.identifier.issn	1939-1374
dc.identifier.uri	http://hdl.handle.net/10453/160417
dc.description.abstract	In a distributed, dynamic and volatile operating environment, runtime anomalies occurring in service-oriented systems (SOSs) must be located and fixed in a timely manner in order to guarantee successful delivery of outcomes in response to user requests. Monitoring all component services constantly and inspecting the entire SOS upon a runtime anomaly are impractical due to excessive resource and time consumption required, especially in large-scale scenarios. We present a spectrum-based approach that goes through a five-phase process to quickly localize runtime anomalies occurring in SOSs based on end-to-end system delays. Upon runtime anomalies, our approach calculates the similarity coefficient for each basic component (BC) of the SOS to evaluate their suspiciousness of being faulty. Our approach also calculates the delay coefficients to evaluate each BC's contribution to the severity of the end-to-end system delays. Finally, the BCs are ranked by their similarity coefficient scores and delay coefficient scores to determine the order of them being inspected. Extensive experiments are conducted to evaluate the effectiveness and efficiency of the proposed approach. The results indicate that our approach significantly outperforms random inspection and the popular Ochiai-based inspection in localizing single and multiple runtime anomalies effectively. Thus, our approach can help save time and effort for localizing runtime anomalies occuring in SOSs.
dc.language	English
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Transactions on Services Computing
dc.relation.isbasedon	10.1109/TSC.2016.2593462
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0803 Computer Software, 0805 Distributed Computing, 0806 Information Systems
dc.title	Localizing Runtime Anomalies in Service-Oriented Systems
dc.type	Journal Article
utslib.citation.volume	10
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-08-17T04:20:58Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	10
utslib.citation.issue	1

Abstract:

In a distributed, dynamic and volatile operating environment, runtime anomalies occurring in service-oriented systems (SOSs) must be located and fixed in a timely manner in order to guarantee successful delivery of outcomes in response to user requests. Monitoring all component services constantly and inspecting the entire SOS upon a runtime anomaly are impractical due to excessive resource and time consumption required, especially in large-scale scenarios. We present a spectrum-based approach that goes through a five-phase process to quickly localize runtime anomalies occurring in SOSs based on end-to-end system delays. Upon runtime anomalies, our approach calculates the similarity coefficient for each basic component (BC) of the SOS to evaluate their suspiciousness of being faulty. Our approach also calculates the delay coefficients to evaluate each BC's contribution to the severity of the end-to-end system delays. Finally, the BCs are ranked by their similarity coefficient scores and delay coefficient scores to determine the order of them being inspected. Extensive experiments are conducted to evaluate the effectiveness and efficiency of the proposed approach. The results indicate that our approach significantly outperforms random inspection and the popular Ochiai-based inspection in localizing single and multiple runtime anomalies effectively. Thus, our approach can help save time and effort for localizing runtime anomalies occuring in SOSs.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/160417