Towards a Trust Prediction Framework for Cloud Services Based on PSO-Driven Neural Network
Towards a Trust Prediction Framework for Cloud Services Based on PSO-Driven Neural Network
复制标题
基于 PSO 驱动神经网络的云服务信任预测框架
DOI：
10.1109/access.2017.2654378
复制
发表时间：
2017-01
期刊：
影响因子：
3.9
通讯作者：
Qiang He
中科院分区：
文献类型：
--
作者：
Chengying Mao;Rongru Lin;Changfu Xu;Qiang He
可信性是云环境下服务选择和推荐的重要指标。然而，云服务的服务质量(Quality of Service，QOS)与服务的最终信任度之间存在复杂的非线性关系，因此基于服务质量(Quality of Service，QOS)对云服务的信任度进行预测并非易事。根据已有的研究，采用智能技术是解决这一问题的合理途径。神经网络已被证明是一种预测服务信任度的有效方法。然而，神经网络的参数设置对其预测性能有着重要的影响，目前还没有得到很好的解决。本文引入粒子群算法(PSO)，通过优化网络的初始设置来增强神经网络的性能。在提出的混合预测算法PSO-NN中，利用PSO为神经网络寻找合适的参数，从而实现对云服务的准确信任预测。为了考察PSO-NN的有效性，在公共服务质量数据集上进行了大量的实验，并进行了深入的比较分析。结果表明，我们提出的方法在大多数情况下都比基本分类方法具有更好的性能，并且在预测精度方面明显优于基本神经网络。另外，PSO-NN比基本神经网络具有更好的稳定性。
Trustworthiness is an important indicator for service selection and recommendation in the cloud environment. However, predicting the trust rate of a cloud service based on its multifaceted quality of services (QoSs) is not an easy task due to the complicated and non-linear relations between service’s QoS values and the final trust rate of the service. According to the existing studies, the adoption of intelligent technique is a rational way to attack this problem. Neural network (NN) has been validated as an effective way to predict the trust rate of the service. However, the parameter setting of NN, which plays an important role in its prediction performance, has not been properly addressed yet. In the paper, particle swarm optimization (PSO) is introduced to enhance NN by optimizing its initial settings. In the proposed hybrid prediction algorithm named PSO-NN, PSO is used to search the appropriate parameters for NN so as to realize accurate trust prediction of cloud services. In order to investigate the effectiveness of PSO-NN, extensive experiments are performed based on public QoS data set, as well as in-depth comparison analysis. The results show that our proposed approach has better performance than basic classification methods in most cases, and significantly outperforms the basic NN in the terms of prediction precision. In addition, PSO-NN demonstrates better stability than the basic NN.
登录
查看更多内容
DOI：
10.1109/icws.2010.36
发表时间：
2010-07
期刊：
2010 IEEE International Conference on Web Services
影响因子：
--
作者：
H. T. Nguyen;Weiliang Zhao;Jian Yang
通讯作者：
H. T. Nguyen;Weiliang Zhao;Jian Yang
DOI：
10.4018/978-1-7998-1192-3.ch008
发表时间：
2020
期刊：
Advances in Systems Analysis, Software Engineering, and High Performance Computing
影响因子：
--
作者：
Menaga D.;R. S.
通讯作者：
Menaga D.;R. S.
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：
10.4018/978-1-5225-9096-5.ch007
发表时间：
2021-07
期刊：
Smart Computational Intelligence in Biomedical and Health Informatics
影响因子：
--
作者：
A. Sinha;S. Gupta;Anurag Tiwari;Amrita Chaturvedi
通讯作者：
A. Sinha;S. Gupta;Anurag Tiwari;Amrita Chaturvedi
DOI：
10.4018/978-1-5225-7862-8.ch007
发表时间：
2019
期刊：
Handbook of Research on Deep Learning Innovations and Trends
影响因子：
--
作者：
K. Lakhtaria;Darshankumar Modi
通讯作者：
K. Lakhtaria;Darshankumar Modi