As an essential feature of cloud computing, dynamic scalability enables the cloud system to dynamically expand or shrink resources according to user needs at runtime. Effectively predicting and optimizing the cost and performance of cloud computing platforms have become one of the key research challenges in the field of cloud computing. In this article, to quantitatively predict the cost and performance of cloud computing platforms, we propose a cloud computing resource analysis model considering both hot/cold startup and hot/cold shutdown of virtual machines (VMs), and use the $M/M/N/\infty$M/M/N/∞ queuing model to analyze cloud computing platform and acquire accurate performance indicators, such as elasticity indicators, cost indicators, performance indicators, cost-performance ratios, etc. In addition, we establish a multi-objective optimization model to optimize both performance and cost of cloud computing platform. Then the optimal stopping and cost-performance optimization algorithm are applied to obtain the optimal configurations, including the number of hot startup VMs, the system service rate, the hot/cold startup rate of VMs, and the hot/cold shutdown rate. By comparing with existing optimization methods, we demonstrate the superiority of our cost-performance ratio optimization method.

中文翻译：

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云计算虚拟机调度建模分析及性价比优化

作为云计算的本质特征，动态可扩展性使云系统能够在运行时根据用户需求动态扩展或收缩资源。有效预测和优化云计算平台的成本和性能已成为云计算领域的关键研究挑战之一。在本文中，为了定量预测云计算平台的成本和性能，我们提出了一种同时考虑虚拟机（VM）的热/冷启动和热/冷关机的云计算资源分析模型，并使用$M/M/N/\infty$米/米/N/∞排队模型分析云计算平台，获取准确的性能指标，如弹性指标、成本指标、性能指标、性价比等。此外，我们建立了多目标优化模型，优化云计算的性能和成本计算平台。然后应用最优停止和性价比优化算法得到最优配置，包括热启动虚拟机数量、系统服务率、虚拟机热/冷启动率和热/冷关闭率。通过与现有优化方法的比较，我们证明了我们的性价比优化方法的优越性。