Search based approach to forecasting QoS attributes of web services using genetic programming

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@Article{Fanjiang:2016:IST,
  author =       "Yong-Yi Fanjiang and Yang Syu and Jong-Yih Kuo",
  title =        "Search based approach to forecasting QoS attributes of
                 web services using genetic programming",
  journal =      "Information and Software Technology",
  volume =       "80",
  pages =        "158--174",
  year =         "2016",
  ISSN =         "0950-5849",
  DOI =          "doi:10.1016/j.infsof.2016.08.009",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0950584916301409",
  abstract =     "AbstractContext Currently, many service operations
                 performed in service-oriented software engineering
                 (SOSE) such as service composition and discovery depend
                 heavily on Quality of Service (QoS). Due to factors
                 such as varying loads, the real value of some dynamic
                 QoS attributes (e.g., response time and availability)
                 changes over time. However, most of the existing
                 QoS-based studies and approaches do not consider such
                 changes; instead, they are assumed to rely on the
                 unrealistic and static QoS information provided by
                 service providers, which may seriously impair their
                 outcomes. Objective To predict dynamic QoS values, the
                 objective is to devise an approach that can generate a
                 predictor to perform QoS forecasting based on past QoS
                 observations. Method We use genetic programming (GP),
                 which is a type of evolutionary computing used in
                 search-based software engineering (SBSE), to forecast
                 the QoS attributes of web services. In our proposed
                 approach, GP is used to search and evolve
                 expression-based, one-step-ahead QoS predictors. To
                 evaluate the performance (accuracy) of our GP-based
                 approach, we also implement most current time series
                 forecasting methods; a comparison between our approach
                 and these other methods is discussed in the context of
                 real-world QoS data. Results Compared with common time
                 series forecasting methods, our approach is found to be
                 the most suitable and stable solution for the defined
                 QoS forecasting problem. In addition to the numerical
                 results of the experiments, we also analyze and provide
                 detailed descriptions of the advantages and benefits of
                 using GP to perform QoS forecasting. Additionally,
                 possible validity threats using the GP approach and its
                 validity for SBSE are discussed and evaluated.
                 Conclusions This paper thoroughly and completely
                 demonstrates that under a realistic situation (with
                 real-world QoS data), the proposed GP-based QoS
                 forecasting approach provides effective, efficient, and
                 accurate forecasting and can be considered as an
                 instance of SBSE.",
  keywords =     "genetic algorithms, genetic programming, SBSE,
                 Search-based software engineering, Web service, Qos
                 attribute forecasting",
}

Genetic Programming entries for Yong-Yi Fanjiang Yang Syu Jong-Yih Kuo

Citations