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首页> 外文会议>International conference on mobile, secure, and programmable networking >Predicting Response Time-Related Quality-of-Service Outages of PaaS Cloud Applications by Machine Learning
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Predicting Response Time-Related Quality-of-Service Outages of PaaS Cloud Applications by Machine Learning

机译：通过机器学习预测PaaS云应用程序的响应时间相关的服务质量中断

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摘要

For customers running their applications on Platform-as-a-Service (PaaS) cloud environments it is important to ensure the Quality-of-Service (QoS) of their applications. Knowing in advance if and when a potential problem is likely to occur allows the application owner to take appropriate countermeasures. Therefore, predictive analytics using machine learning could allow to be alerted in advance about potential upcoming QoS outages. In this context, mainly Infrastructure-as-a-Service (IaaS) or Software-as-a-Service (SaaS) have been studied in the literature so far. Studies about predicting QoS outages for the Platform-as-a-Service (PaaS) service model are sparse. Therefore, in this paper an approach for predicting response-time-related QoS outages of web services running in a PaaS cloud environment is presented. The proposed solution uses the open source Apache Spark platform in combination with MLib and binary classification by the naive Bayes algorithm. The approach is evaluated by using test data from a social app backend web service. The results indicate that it is feasible in practice.
机译：对于在平台即服务（PaaS）云环境上运行其应用程序的客户，重要的是确保其应用程序的服务质量（QoS）。事先知道是否以及何时可能发生潜在问题，使应用程序所有者可以采取适当的对策。因此，使用机器学习的预测分析可以允许提前就潜在的即将到来的QoS中断发出警报。在这种情况下，到目前为止，文献中主要研究了基础架构即服务（IaaS）或软件即服务（SaaS）。稀疏的关于平台即服务（PaaS）服务模型的QoS中断预测的研究很少。因此，本文提出了一种预测在PaaS云环境中运行的Web服务的响应时间相关QoS中断的方法。提出的解决方案结合了朴素的贝叶斯算法，将开源Apache Spark平台与MLib和二进制分类结合使用。通过使用来自社交应用程序后端Web服务的测试数据来评估该方法。结果表明，该方法在实践中是可行的。

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