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

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@Article{FanJiang:2020:SC,

• author = "Yong-Yi FanJiang and Yang Syu and Wei-Lun Huang",
• journal = "IEEE Transactions on Services Computing",
• title = "Time Series {QoS} Forecasting for Web Services Using Multi-Predictor-based Genetic Programming",
• year = "2020",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/TSC.2020.2994136",
• ISSN = "1939-1374",
• abstract = "Quality of service (QoS) time-series forecasting of web services has been studied for over a decade, and in recent years, this problem has been investigated in its multi-step-ahead version for the long-term rental and use of cloud computing. For multi-step-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 models and exponential smoothing, to solve this problem. However, this paper proposes the idea of applying genetic programming to evolve a set of multiple predictors, in which each predictor is dedicated to the forecasting task of a specific future time point. In our approach, two types of multiple predictors are proposed and tested, which are different from the consumed predictor inputs that drive each predictor to produce its QoS forecasting results. Furthermore, two techniques, namely, elite individual composition (EIC) and hybrid evolution, are proposed and applied 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 conventional methods to demonstrate its superiority in terms of accuracy; in addition, the effectiveness and efficiency of the proposed approach and two techniques are also verified in the experiment.",
• notes = "Also known as \cite{9091301}",

}

Genetic Programming entries for Yong-Yi FanJiang Yang Syu Wei-Lun Huang

Citations