为了进一步分析QoS历史数据的动态变化,对现有的基于QoS历史数据的云服务选择算法进行了改进.将原算法中每一时间段的评价指标权重由QoS历史数据平均值获得,修改为由该时间段对应的QoS历史数据获得,更能发挥历史数据的动态性.使用时间序列预测ARIMA模型对原QoS历史数据进行预测,把预测结果并入原数据集形成新的数据集,在新数据集上进行服务选择.设计了三个模型递进地进行实验分析,通过对比实验结果验证了改进算法的性能效果.%For further analysis of the frequent variation in the QoS performance, the existing cloud service selection based on QoS historical data is improved. Firstly, in the original algorithm, the criteria weights of service decision-making in each period are calculated by entropy method based on the average QoS historical data. To further capture the frequent variation in QoS performance, the process to obtain the criteria weights is improved, this paper prefers to calculate the weights by entropy method based on the period's QoS data rather than based on the overall average QoS historical data. Secondly, it uses time series forecasting ARIMA model to forecast the future QoS data, these data can be added into the original data set to generate a new data set, and then service decision-making is done based on the new data set. Finally, in order to evaluate two improvements, three experimental models are proposed for experiment progressively, then it analyzes the performance of the improved algorithm based on the comparison of experimental results.
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