一种均衡风险与偏好的并行服务选择算法

A Parallel Service Selection Algorithm Balancing Risk and Preference

针对传统考虑QoS序列均值(偏好)的QoS序列描述方法缺乏对QoS序列波动(风险)的描述问题,提出一种结合风险与偏好的并行服务选择算法。通过静态QoS模型转换器和分布式QoS模型转换器,将QoS数值序列转换为基于均值标准差的QoS模型。在此基础上,设计QoS模型自适应调整机制以适应QoS的动态变化,并利用基于逼近理想解排序法(TOPSIS)的并行服务选择算法获得体现用户需求的最优服务。实验结果表明,该算法提高了服务选择的可靠性,并且能解决大QoS数据序列的不确定性问题。

Traditional Quality of Service （QoS） sequence description method only considers the mean （preference） which cannot reflect the QoS fluctuation （risk）. To solve the problem, a parallel service selection algorithm combining risk and preference is proposed. The QoS value sequence is converted to the QoS model based on standard error of mean by using the static QoS model converter and the distributed QoS model converter. Based on the QoS model, an adaptive adjustment mechanism is designed to adapt dynamic change of QoS. The service selection algorithm based on Technique for Order Preference by Similarity to Ideal Solution（TOPSIS） is designed to obtain the optimal service reflecting users＇ QoS needs. Experimental results show that the proposed algorithm improves the reliability of service selection and can solve the uncertainty problem of big QoS data sequence.