多元时间序列的Web Service QoS预测方法
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张鹏程(1981-),男,江苏滨海人,博士,副教授,CCF高级会员,主要研究领域为软件建模、分析和验证技术;王丽艳(1992-),女,硕士,主要研究领域为多元时间序列预测;吉顺慧(1987-),女,博士,讲师,CCF专业会员,主要研究领域为软件建模、分析、测试与验证;李雯睿(1981-),女,博士,副教授,CCF高级会员,主要研究领域为服务计算.

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张鹏程,E-mail:pchzhang@hhu.edu.cn

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基金项目:

国家自然科学基金(61572171,61702159,61202097);江苏省自然科学基金(BK20170893);中央高校基本科研业务费(2019B15414)


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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    摘要:

    为准确并多步预测Web服务的服务质量(quality of service,简称QoS),方便用户选择更好的Web服务,提出了一种基于多元时间序列的QoS预测方法MulA-LMRBF (multiple step forecasting with advertisement-levenberg marquardt radial basis function).充分考虑多个QoS属性序列之间的关联,采用平均位移法(average dimension,简称AD)确定相空间重构的嵌入维数和延迟时间,将QoS属性历史数据映射到一个动力系统中,近似恢复多个QoS属性之间的多维非线性关系.将短期服务提供商QoS广告数据加入数据集中,采用列文伯格-马夸尔特法(Levenberg-Marquardt,简称LM)算法改进的径向基(radial basis function,简称RBF)神经网络预测模型,动态更新神经网络的权重,提高预测精度,实现QoS动态多步预测.通过网络开源数据和自测数据的实验结果表明,该方法与传统方法相比有较好预测效果,更适合动态多步预测.

    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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  • 收稿日期:2017-02-24
  • 最后修改日期:2017-08-17
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  • 在线发布日期: 2017-11-29
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