基于二阶隐马尔科夫模型的云服务QoS满意度预测

QoS Satisfaction Prediction of Cloud Service Based on Second Order Hidden Markov Model

随着云计算相关技术的迅速发展,云服务组件的QoS预测成为云计算中一个重要的研究课题。实现对QoS值的准确预测是该领域的研究难点。QoS常用来衡量不同云服务组件的性能,基于不同候选组件的QoS值,可以容易地选出最优的组件。对于同一个云服务组件,不同的用户提供的QoS值并不一定相同。针对不同的用户,有个性化的组件QoS值才能进行准确的选择。如果用户的QoS不能由单一的云服务组件满足,则应该考虑组件组合,在这种情况下,需要预测其QoS能力,以保证用户需求得到满足。文中设计了云服务组件的QoS满意预测模型,该模型使用二阶隐马尔科夫模型构建QoS满意度预测模型,通过考虑前两个状态对当前状态的影响,能够有效提高预测精度。最后,通过所构建的原型系统和具有2507个真实Web服务的QWS数据集,并应用Matlab仿真环境验证了所提方法的有效性。

With the rapid development of cloud computing technology,QoS prediction of cloud service components has become an important research issue in cloud computing.Accurate prediction of the QoS value is of great difficulty in this research field.QoS is often used to measure the performance of different cloud service components.Based on the QoS values of different candidate components,it is easy to choose the best one.For the same cloud service component,the QoS values provided by different users are not necessarily the same.For different users,personalized component QoS values are needed so that accurate selection can be made.If the user’s QoS cannot be satisfied by a single cloud service component,the component composition should be considered.In this case,its QoS capability should to be predicted to meet the user’s needs.This paper presented a QoS satisfied prediction model of cloud service component.The model uses a second order hidden markov model to construct the QoS satisfaction predictive model .By considering the in- flue nce of the previous two states on the current state,the proposed method can effectively improve the prediction accuracy .Finally,in the Matlab simulation experiment environment,the effectiveness of the proposed method is prove by the prototype system and QWS data set with 2 507 real web services.