An Econometric Time Series Forecasting Framework for Web Services Recommendation

Abstract

The convergence of SMAC technologies resulted in an unexpected upsurge of web services on the internet. The flexibility and rental approach of the cloud makes it an attractive option for the deployment of web services-based applications. Once a number of web services are available to gratify the similar functionalities, then the choice of the web service based on personalized quality of service (QoS) parameters plays an important role in deciding the selection of the web service. The role of time is rarely being discussed in deciding the QoS of web services. The delivery of QoS is not made as declared due to the correlated behavior of non-functional performance of web services with the invocation time. This happens because service status usually changes over time. These limitations have affected the performance of neighborhood-based collaborative filtering. Hence, the design of the time aware web service recommendation system based on the personalized QoS parameters is very crucial and turn out to be a challenging research issue. In the current work, various econometric models are used for experimentation with the input time series to find the best fit model for the prediction of personalized QoS based web services recommendation. The Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) value is used as an evaluation metric and their value for the prediction of Response time is found to be 1.7392e+04 and 1.7416e+04 respectively with the ARMA model. The AIC and BIC value for throughput (TP) is found to be 1.5249e+04 and 1.5334e+04 respectively with the ARIMA. The value of variance is 13.2437 and 6.8131 for RT and TP respectively which is also the lowest among other models with t-statistic greater than the p-value. Thus, the experimental results show that the ARMA and ARIMA model of Time Series Forecasting for Web Services Recommendation Framework is performing better in case of Response time and throughput respectively.