基于ARIMA和卡尔曼滤波的在线Web服务Qo S预测方法

The Web services QoS prediction method based on ARIMA and Kalman filtering

随着Web服务使用的广泛,人们普遍发现,Web服务的服务质量(Quality-of-Service,QoS)受网络环境、服务端负载等诸多因素影响不断变化,而保证服务使用过程中的QoS也成为许多Web服务使用者的普遍要求。如何更好地帮助服务使用者选择未来一段时间内符合其服务质量要求的Web服务,同时也帮助服务提供者避免服务质量的违规,是服务计算领域近年来的热点问题。由于ARIMA(Autoregressive Integrated Moving Average Model)模型参数简单并能较好地预测QoS违规,已经在Web服务的QoS预测领域获得了广泛的应用。但是单纯地使用ARIMA模型不能够适应Web服务QoS数据的波动频繁、包含噪声等复杂特点。为了达到更加准确的预测效果,本文提出了一种基于时间序列分析的Web服务QoS预测方法,该方法结合了ARIMA模型与卡尔曼滤波,对服务质量的波动反馈灵敏,较单一的预测模型能够有更准确的预测效果。

With the wide use of Web services,it is generally found that the QoS(quality of service)of Web services is constantly changing due to various factors such as the network environment and servers'load.Business users commonly claim that QoS should be guaranteed.How to help service users select Web services that meet the quality of service requirements for a period of time in the future,and also help service providers to avoid service quality violations are the hot issue in the field of service computing in recent years.This paper proposes a Web service QoS prediction method based on time series analysis.This method combines ARIMA model and Kalman Filtering.Meanwhile it is sensitive to the fluctuation of service quality,and can have more accurate prediction effect than a single prediction model.