Increasing Dependability of Component-based Software Systems by Online Failure Prediction

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. Keywords—dependability, online failure prediction, componentbased software systems, monitoring, models at runtime