Time Series QoS Forecasting for Web Services Using Multi-Predictor-Based Genetic Programming

Quality of service (QoS) time series forecasting for web services (WSs) has been studied for over a decade. In recent years, this problem has been investigated in its multistep-ahead version (namely, the problem where the prediction horizon is greater than one) for the long-term rental and use of cloud-based WSs. To solve this multistep-ahead QoS time series forecasting problem, previous research has adopted single-predictor-based strategies and conventional time series methods, such as autoregressive integrated moving average (ARIMA) models and exponential smoothing. In this article, however, we propose applying genetic programming (GP) to search for and evolve a set of multiple predictors, in which each predictor is dedicated to forecasting a specific future time point. Our GP-based approach proposes and tests two types of multiple predictors that differ from the consumed predictor inputs that drive each predictor to produce its QoS forecasting results. In the first type, the input of each predictor is a sequence of fixed previous QoS observations; in the second type, each predictor dynamically consumes the most recent QoS values, which could consist of the forecasting results of its previous predictors). Furthermore, we propose two techniques for our multipredictor-based GP approach, namely, elite individual composition (EIC) and hybrid evolution, and apply them to enhance the forecasting accuracy of our approach. Finally, based on a real-world QoS time series dataset, the proposed approach is validated and compared with several conventional methods to demonstrate its superiority in terms of accuracy; in addition, the effectiveness and efficiency of the proposed multiple predictors and the two techniques are verified in the experiment.