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What-if QoS Prediction of Cloud-hosted Web Services via Domain Adaptation in Evolutionary Scenarios

机译:在进化方案中通过域适应域对云托管Web服务的QoS预测

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In order to ensure that the web application can continue to provide high-quality services after implementing the bandwidth management schemes, the administrators usually need to predict the Quality of Service(QoS) in advance according to the hypothetical changes of the web system. In the existing research, few pay attention to the prediction of QoS in bandwidth-driven evolutionary scenarios. In this paper, we propose a solution comprised of automated data mining skills and transfer learning techniques to predict the response time of web services in bandwidth-driven evolutionary scenarios. We choose a suitable approach of domain adaptation according to the characteristics of the QoS prediction problems in this paper. There are mainly three contributions in this paper: 1) we adopt an automated data mining approach to extract features for prediction. 2) To our knowledge, it is the first time to apply domain adaptation to the QoS prediction of web services in evolutionary scenarios. 3) We perform some experiments to evaluate the effectiveness and stability of the proposed solution, including two types of evolutionary scenarios under a realworld web application.
机译:为了确保Web应用程序在实现带宽管理方案后继续提供高质量的服务,管理员通常需要根据Web系统的假设变化预先预测服务质量(QoS)。在现有的研究中,很少注意带宽驱动的进化方案中的QoS的预测。在本文中,我们提出了一种由自动数据挖掘技能和传输学习技术组成的解决方案,以预测带宽驱动的进化方案中Web服务的响应时间。我们根据本文QoS预测问题的特征选择了一个合适的域适应方法。本文主要有三个贡献:1)我们采用自动化数据挖掘方法来提取预测的特征。 2)对于我们的知识,这是第一次将域改编应用于进化方案中Web服务的QoS预测。 3)我们执行一些实验来评估所提出的解决方案的有效性和稳定性,包括在RealWorld Web应用程序下的两种类型的进化方案。

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