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

Su, Guoxin, Chen, Taolue, Feng, Yuan and Rosenblum, David S. (2017) ProEva: runtime proactive performance evaluation based on continuous-time markov chains. In: 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE), 20-28 May 2017, Buenos Aires, Argentina. (doi:10.1109/ICSE.2017.51)

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Abstract

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.

Item Type: Conference or Workshop Item (Paper)
Research Areas: A. > School of Science and Technology > Computer Science > Foundations of Computing group
Item ID: 22022
Useful Links:
Depositing User: Taolue Chen
Date Deposited: 15 Jun 2017 16:22
Last Modified: 14 May 2018 14:13
ISBN: 9781538638682
URI: https://eprints.mdx.ac.uk/id/eprint/22022

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