Modeling analysis and cost-performance ratio optimization of virtual machine scheduling in cloud computing

Abstract

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/∞ 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.