A Short-Term Prediction for QoS of Cloud Manufacturing Based on Improved Support Vector Regression Algorithm
A Short-Term Prediction for QoS of Cloud Manufacturing Based on Improved Support Vector Regression Algorithm
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DOI：
10.14257/ijunesst.2016.9.6.28
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发表时间：
2016-06
期刊：
影响因子：
--
通讯作者：
Shumei Liu
中科院分区：
文献类型：
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作者：
Shumei Liu
云制造结合了信息制造、物联网（IoT）、云计算和语义网技术。通过对服务技术和网络制造的延伸和变革，使制造资源和制造能力虚拟化、服务化。可对生产制造进行集中统一的智能化管理。因此，建立合适的云制造服务质量模型是开展云制造相关研究的基础和前提。现有的QoS模型忽略了时间对QoS的影响。这直接导致了建模的准确性不足，进而影响了后续研究在预测、服务选择等方面的实施效果。结合云制造的评价标准，提出了一种与云制造背景相一致的五维QoS评价标准及相应的计算公式。其次，建立了基于任意惩罚带的支持向量机的QoS预测模型。该模型弥补了以往研究的不足，有效地解决了云制造中QoS预测的维度问题。第三，本文预测了5个重要的QoS指标。实验数据来源于某云制造提供商的历史数据，实验中使用了大量的历史QoS值来训练具有任意惩罚带的支持向量机。当系数大于0.995时，终止网络训练。然后，利用样本数据集的QoS值预测未来七个节点的QoS值。最后通过实验表明，任意惩罚的支持向量回归机对云制造的QoS具有较好的预测效果。而人工神经网络和灰色预测模型对预测效果较差。
Cloud manufacturing combined with information manufacturing, the Internet of things(IoT), cloud computing, and semantic web technology. Through extending and changing the service technology and network manufacturing, it makes the manufacturing resources and manufacturing capabilities virtualization and servicization. It can make centralized and unified intelligent management for manufacturing. Therefore, the establishment of appropriate cloud manufacturing QoS(quality of service) model is the basis and prerequisite for the development of related research of cloud manufacturing. The existing QoS model ignored the effect of time on QoS. This directly leads to the lack of accuracy of modeling, and further affects the implementation effect of the follow-up study on the prediction, service selection and so on. With cloud manufacturing evaluation criteria, this paper proposes a five dimensional QoS evaluation criteria and the corresponding calculation formula, it is consistent with the cloud manufacturing background. Secondly, we establish the QoS prediction model based on the support vector machine with arbitrary penalty band. This model can cover the shortage for the previous studies, and effectively solve the problem of the dimension of QoS prediction in cloud manufacturing. Thirdly, this paper predicts 5 important QoS indexes. The experimental data from a cloud manufacturing provider's historical data, and the experimental use a large number of historical QoS value to train the support vector machine with arbitrary penalty band. Network training is terminated when the coefficient is greater than 0.995. Then, the QoS value of the seven nodes in the future is predicted by the QoS value of the sample data set. Finally, the experiment shows that the support vector regression machine with arbitrary penalty has a good prediction effect on QoS of cloud manufacturing. But the artificial neural network and the gray forecast model has poor effect on it.