为正确预测Web Service的服务质量(Quality of Service,QoS),帮助用户选择符合服务质量需求的Web Service,提出一种基于径向基神经网络模型的服务质量组合预测方法。首先使用时间序列模型对数据集建立线性和非线性预测模型,并选择最优模型,同时根据数据特点建立不同滑动窗口的灰色等维新息模型,再将上述2模型的预测结果作为输入源传递给径向基神经网络的训练模型,进行预测。实验结果表明,该方法与已有方法相比较,在预测精度方面有一定程度的提高。
As an important factor in evaluating service,QoS(Quality of Service) has drawn more and more concerns with the rapid increasing of Web services. However,due to the great volatility of services in Mobile Internet environments,such as internet of vehicles,Web services often do not work as announced and thus cause unacceptable problems. QoS prediction can avoid failure before it takes place,which is considered a more effective way to assure quality. However,Current QoS prediction approaches neither consider the highly dynamic of Web services,nor maintain good prediction performance all the time. Consequently we propose a novel Bayesian combinational model to predict QoS by continuously adjusting credit values of the basic models so as to keep good prediction accuracy. QoS attributes such as response time,throughput and reliability are used to validate the proposed model. Experimental results show that the model can provide stable prediction results in Mobile Internet environments.
Pengcheng ZhangYingtao SunHareton LeungMeijun XuWenrui Li
