Time Series QoS Forecasting for Web Services Using Multi-Predictor-based Genetic Programming
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- @Article{FanJiang:2020:SC,
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author = "Yong-Yi FanJiang and Yang Syu and Wei-Lun Huang",
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journal = "IEEE Transactions on Services Computing",
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title = "Time Series {QoS} Forecasting for Web Services Using
Multi-Predictor-based Genetic Programming",
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year = "2020",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/TSC.2020.2994136",
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ISSN = "1939-1374",
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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.",
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notes = "Also known as \cite{9091301}",
- }
Genetic Programming entries for
Yong-Yi FanJiang
Yang Syu
Wei-Lun Huang
Citations