ProEva: Runtime proactive performance evaluation based on continuous-time markov chains

Su, G; Chen, T; Feng, Y; Rosenblum, DS

ProEva: Runtime proactive performance evaluation based on continuous-time markov chains

Su, G Chen, T Feng, Y

Rosenblum, DS

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2017 IEEE/ACM 39th International Conference on Software Engineering, ICSE 2017, 2017, pp. 484 - 495
Issue Date:: 2017-07-19

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Field	Value	Language
dc.contributor.author	Su, G	en_US
dc.contributor.author	Chen, T	en_US
dc.contributor.author	Feng, Y https://orcid.org/0000-0002-3097-3896	en_US
dc.contributor.author	Rosenblum, DS	en_US
dc.date.issued	2017-07-19	en_US
dc.identifier.citation	Proceedings - 2017 IEEE/ACM 39th International Conference on Software Engineering, ICSE 2017, 2017, pp. 484 - 495	en_US
dc.identifier.isbn	9781538638682	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127384
dc.description.abstract	© 2017 IEEE. Software systems, especially service-based software systems, need to guarantee runtime performance. If their performance is degraded, some reconfiguration countermeasures should be taken. However, there is usually some latency before the countermeasures take effect. It is thus important not only to monitor the current system status passively but also to predict its future performance proactively. Continuous-Time Markov chains (CTMCs) are suitable models to analyze time-bounded performance metrics (e.g., how likely a performance degradation may occur within some future period). One challenge to harness CTMCs is the measurement of model parameters (i.e., transition rates) in CTMCs at runtime. As these parameters may be updated by the system or environment frequently, it is difficult for the model builder to provide precise parameter values. In this paper, we present a framework called ProEva, which extends the conventional technique of time-bounded CTMC model checking by admitting imprecise, interval-valued estimates for transition rates. The core method of ProEva computes asymptotic expressions and bounds for the imprecise model checking output. We also present an evaluation of accuracy and computational overhead for ProEva.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP160101652
dc.relation	http://purl.org/au-research/grants/arc/DP130102764
dc.relation.ispartof	Proceedings - 2017 IEEE/ACM 39th International Conference on Software Engineering, ICSE 2017	en_US
dc.relation.isbasedon	10.1109/ICSE.2017.51	en_US
dc.title	ProEva: Runtime proactive performance evaluation based on continuous-time markov chains	en_US
dc.type	Conference Proceeding
utslib.for	0803 Computer Software	en_US
pubs.embargo.period	Not known	en_US
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/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2017 IEEE. Software systems, especially service-based software systems, need to guarantee runtime performance. If their performance is degraded, some reconfiguration countermeasures should be taken. However, there is usually some latency before the countermeasures take effect. It is thus important not only to monitor the current system status passively but also to predict its future performance proactively. Continuous-Time Markov chains (CTMCs) are suitable models to analyze time-bounded performance metrics (e.g., how likely a performance degradation may occur within some future period). One challenge to harness CTMCs is the measurement of model parameters (i.e., transition rates) in CTMCs at runtime. As these parameters may be updated by the system or environment frequently, it is difficult for the model builder to provide precise parameter values. In this paper, we present a framework called ProEva, which extends the conventional technique of time-bounded CTMC model checking by admitting imprecise, interval-valued estimates for transition rates. The core method of ProEva computes asymptotic expressions and bounds for the imprecise model checking output. We also present an evaluation of accuracy and computational overhead for ProEva.

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http://hdl.handle.net/10453/127384