Search based approach to forecasting QoS attributes of web services using genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @Article{Fanjiang:2016:IST,
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author = "Yong-Yi Fanjiang and Yang Syu and Jong-Yih Kuo",
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title = "Search based approach to forecasting QoS attributes of
web services using genetic programming",
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journal = "Information and Software Technology",
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volume = "80",
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pages = "158--174",
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year = "2016",
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ISSN = "0950-5849",
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DOI = "doi:10.1016/j.infsof.2016.08.009",
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URL = "http://www.sciencedirect.com/science/article/pii/S0950584916301409",
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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.",
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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