Providing a pool of various resources and services to customers on the Internet in exchanging money has made cloud computing as one of the most popular technologies. Management of the provided resources and services at the lowest cost and maximum profit is a crucial issue for cloud providers. Thus, cloud providers proceed to auto‐scale the computing resources according to the users' requests in order to minimize the operational costs. Therefore, the required time and costs to scale‐up and down computing resources are considered as one of the major limits of scaling which has made this issue an important challenge in cloud computing. In this paper, a new approach is proposed based on MAPE‐K loop to auto‐scale the resources for multilayered cloud applications. K‐nearest neighbor (K‐NN) algorithm is used to analyze and label virtual machines and statistical methods are used to make scaling decision. In addition, a resource allocation algorithm is proposed to allocate requests on the resources. Results of the simulation revealed that the proposed approach results in operational costs reduction, as well as improving the resource utilization, response time, and profit.

中文翻译：

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具有 K 近邻资源缩放和基于优先级的资源分配的多层云应用的自主资源供应

在互联网上向客户提供各种资源和服务的池以交换货币使云计算成为最流行的技术之一。以最低的成本和最大的利润管理所提供的资源和服务是云提供商的一个关键问题。因此，云提供商根据用户的请求自动扩展计算资源，以最大限度地降低运营成本。因此，扩展和缩减计算资源所需的时间和成本被认为是扩展的主要限制之一，这使得这个问题成为云计算中的一个重要挑战。在本文中，提出了一种基于 MAPE-K 循环的新方法来自动扩展多层云应用程序的资源。K-最近邻（K-NN）算法用于分析和标记虚拟机，并使用统计方法做出缩放决策。此外，提出了一种资源分配算法来分配对资源的请求。模拟结果表明，所提出的方法降低了运营成本，并提高了资源利用率、响应时间和利润。