Multi-valued collaborative QoS prediction for cloud service via time series analysis
Multi-valued collaborative QoS prediction for cloud service via time series analysis
复制标题
基于时间序列分析的云服务多值协同QoS预测
DOI：
10.1016/j.future.2016.10.012
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发表时间：
2017-03
影响因子：
7.5
通讯作者：
Dong Pingping
中科院分区：
文献类型：
--
作者：
Ma Hua;Zhu Haibin;Hu Zhigang;Tang Wensheng;Dong Pingping
针对用户特征的多样性、服务质量(QoS)的不确定性和变化特征，利用云服务的连续监测数据，提出一种多值协同方法，通过时间序列分析预测潜在用户的未知QoS值。该方法将来自消费者的单值数据和时间序列数据组成的多值QoS评估转化为云模型，并基于这些云模型来衡量每个时期潜在用户与其他消费者之间的差异。针对现有云模型间相似度度量方法的不足，提出一种结合方向相似度和维度相似度的向量比较方法，以提高相似度计算的精度。采用模糊层次分析法帮助潜在用户确定各时段的客观权重，并根据多时段QoS评价的综合相似度为潜在用户选择相邻用户。通过将多值QoS评估与多个周期之间的客观权重相结合，预测结果可以与QoS的周期性变化保持一致。最后，基于真实数据集的实验表明，该方法可以为云计算范式中的多值评估提供高精度的协作 QoS 预测。
Aiming at the diversity of user features, the uncertainty and the variation characteristics of quality of service (QoS), by exploiting the continuous monitoring data of cloud services, this paper proposes a multi-valued collaborative approach to predict the unknown QoS values via time series analysis for potential users. In this approach, the multi-valued QoS evaluations consisting of single-value data and time series data from consumers are transformed into cloud models, and the differences between potential users and other consumers in every period are measured based on these cloud models. Against the deficiency of existing methods of similarity measurement between cloud models, this paper presents a new vector comparison method combining the orientation similarity and dimension similarity to improve the precision of similarity calculation. The fuzzy analytic hierarchy process method is used to help potential users determine the objective weight of every period, and the neighboring users are selected for the potential user according to their comprehensive similarities of QoS evaluations in multiple periods. By incorporating the multi-valued QoS evaluations with the objective weights among multiple periods, the predicted results can remain consistent with the periodic variations of QoS. Finally, the experiments based on a real-world dataset demonstrate that this approach can provide high accuracy of collaborative QoS prediction for multi-valued evaluations in the cloud computing paradigm.
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DOI：
10.1504/ijwgs.2015.067156
发表时间：
2015
期刊：
Int. J. Web Grid Serv.
影响因子：
--
作者：
Jianwei Yin;Yueshen Xu
通讯作者：
Jianwei Yin;Yueshen Xu
DOI：
10.1007/s00170-014-6112-0
发表时间：
2014-07
期刊：
The International Journal of Advanced Manufacturing Technology
影响因子：
--
作者：
Changsong Li;Shilong Wang;Ling Kang;Liang Guo;Yang Cao
通讯作者：
Changsong Li;Shilong Wang;Ling Kang;Liang Guo;Yang Cao
DOI：
10.1016/j.future.2018.05.086
发表时间：
2018-11
期刊：
Future Gener. Comput. Syst.
影响因子：
--
作者：
Gabriel G. Castañé;Huanhuan Xiong;Dapeng Dong;J. Morrison
通讯作者：
Gabriel G. Castañé;Huanhuan Xiong;Dapeng Dong;J. Morrison
DOI：
--
发表时间：
2004
期刊：
--
影响因子：
--
作者：
Deyu Li
通讯作者：
Deyu Li
DOI：
--
发表时间：
2011
期刊：
Control and Decision
影响因子：
--
作者：
Guo Chong-hui
通讯作者：
Guo Chong-hui