Cloud computing provides a set of resources and services for customers on the Internet on demand and based on a pay as you go model. Cloud providers are looking to decrease costs and increase profits. Therefore, resource management and provisioning are very important for cloud providers. Automated scaling can be used to provide resources for user requests. Auto‐scaling can decrease the total operational costs for providers, although it does have its own cost and time overheads. In this paper, a new solution is presented for resource provisioning on multi‐layered cloud applications based on MAPE‐K loop. A weighted ensemble prediction model is proposed to estimate the resources utilization in each cloud layer. In addition, accuracy of the model and a regularization technique are used to regulate the weights of the models in the proposed hybrid prediction model. Furthermore, a decision tree‐based algorithm is presented to analyze status of the resources to make scaling decision. In addition, we propose a resource allocation algorithm that is based on Virtual Machine priority and request deadline in order to allocate requests on suitable resources. The experimental results indicate that the proposed algorithm has the best performance among its counterparts.

中文翻译：

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云应用程序中基于自主决策树和限期约束的资源配置

云计算基于按需付费模型，为Internet上的客户按需提供了一套资源和服务。云提供商正在寻求降低成本并增加利润。因此，资源管理和供应对于云提供商非常重要。自动缩放可用于为用户请求提供资源。自动缩放可以降低提供商的总运营成本，尽管它确实有其自身的成本和时间开销。本文针对基于MAPE-K循环的多层云应用程序上的资源供应提出了一种新的解决方案。提出了加权集成预测模型来估计每个云层中的资源利用率。此外，在所提出的混合预测模型中，使用模型的准确性和正则化技术来调节模型的权重。此外，提出了一种基于决策树的算法来分析资源状态以做出扩展决策。此外，我们提出了一种基于虚拟机优先级和请求期限的资源分配算法，以便在合适的资源上分配请求。实验结果表明，该算法在同类算法中性能最好。