NDLI: Increasing Dependability of Component-Based Software Systems by Online Failure Prediction (Short Paper)

Content Provider	IEEE Xplore Digital Library
Author	Pitakrat, T. Van Hoorn, A. Grunske, L.
Copyright Year	2014
Description	Author affiliation: Inst. of Software Technol., Univ. of Stuttgart, Stuttgart, Germany (Pitakrat, T.; Van Hoorn, A.; Grunske, L.)
Abstract	Online failure prediction for large-scale software systems is a challenging task. One reason is the complex structure of many-partially inter-dependent-hardware and software components. State-of-the-art approaches use separate prediction models for parameters of interest or a monolithic prediction model which includes different parameters of all components. However, they have problems when dealing with evolving systems. In this paper, we propose our preliminary research work on online failure prediction targeting large-scale component-based software systems. For the prediction, three complementary types of models are used: (i) an architectural model captures relevant properties of hardware and software components as well as dependencies among them, (ii) for each component, a prediction model captures the current state of a component and predicts independent component failures in the future, (iii) a system-level prediction model represents the current state of the system and-using the component-level prediction models and information on dependencies-allows to predict failures and analyze impacts of architectural system changes for proactive failure management.
Starting Page	66
Ending Page	69
File Size	160355
Page Count	4
File Format	PDF
ISBN	9781479938049
DOI	10.1109/EDCC.2014.28
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-13
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Analytical models models at runtime Runtime Computational modeling component-based software systems dependability Quality of service Predictive models Data models Software monitoring online failure prediction
Content Type	Text
Resource Type	Article

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