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Home > Archive>Volume 30, Issue 6, 2019 >1742-1758. DOI:10.13328/j.cnki.jos.005425
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Web Service QoS Forecasting Approach Using Multivariate Time Series
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National Natural Science Foundation of China (61572171, 61702159, 61202097); Natural Science Foundation of Jiangsu Province, China (BK20170893); Fundamental Research Funds for the Central Universities of China (2019B15414)

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    Abstract:

    In order to accurately forecast quality of service (QoS) of different Web services with multi-step, and help users to choose the most suitable Web service at hand, this study proposes a novel QoS forecasting approach called MulA-LMRBF (multiple-step forecasting with advertisement by levenberg-marquardt improved radial basis function network) based on multivariate time series. Considering the correlation among different QoS attributes series, phase-space reconstruction is used to map historical multivariate QoS data into a dynamic system, where the multi-dimensional nonlinear relations of QoS attributes are completely restored. Average dimension (AD) is used to estimate the embedding dimension and delay time of reconstructed phase space. The short-term QoS advertisement data of service provider is also added to form a more comprehensive data set. Then, RBF (radial basis function) neural network improved by the Levenberg-Marquardt (LM) algorithm is used to update the weight of the neural network dynamically, which improves the forecasting accuracy and realizes the dynamic multiple-step forecasting. Experiments are conducted based on several public network data sets and self-collected data set. The experimental results demonstrate that MulA-LMRBF is better than previous approaches with high precise and is more suitable for multi-step forecasting.

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张鹏程,王丽艳,吉顺慧,李雯睿.多元时间序列的Web Service QoS预测方法.软件学报,2019,30(6):1742-1758

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  • Received:February 24,2017
  • Revised:August 17,2017
  • Online: November 29,2017
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