Abstract
OWL-S, one of the most important Semantic Web service ontologies proposed to date, provides a core ontological framework and guidelines for describing the properties and capabilities of their web services in an unambiguous, computer interpretable form. Predicting the reliability of composite service processes specified in OWL-S allows service users to decide whether the process meets the quantitative quality requirement. In this study, we consider the runtime quality of services to be fluctuating and introduce a dynamic framework to predict the runtime reliability of services specified in OWL-S, employing the Non-Markovian stochastic Petri net (NMSPN) and the time series model. The framework includes the following steps: obtaining the historical response times series of individual service components; fitting these series with a autoregressive-moving-average-model (ARMA for short) and predicting the future firing rates of service components; mapping the OWL-S process into a NMSPN model; employing the predicted firing rates as the model input of NMSPN and calculating the normal completion probability as the reliability estimate. In the case study, a comparison between the static model and our approach based on experimental data is presented and it is shown that our approach achieves higher prediction accuracy.
Highlights
Web Services are interfaces that describe a collection of operations that are network-accessible through standardized protocols
We introduce the analytical methods to predict reliability of OWL-S process. This method takes the predicted fire rates and the Non-Markovian stochastic Petri net (NMSPN) representations as model inputs
We present a comprehensive dependability prediction model for OWL-S processes
Summary
Web Services are interfaces that describe a collection of operations that are network-accessible through standardized protocols. Various studies have discussed how to effectively model and predict the reliability of service compositions based on the aggregations of the reliabilities of its constituent activities. These studies share a common idea that they all try to fit historical reliability data of activities into assumed distributions (deterministic, exponential, geometrical, or general distributions) and employ these obtained distributions as the static model input into the static stochastic models (continuous Markovian model, discrete Markovian model, or PERT (Program Evaluation and Review Technique, [2]) mode, etc.) to obtain the predicted reliability. The comparison suggests that our dynamic prediction model produces less errors and achieves higher prediction accuracy
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