This paper presents a survey on quality of service (QoS) in cloud computing, focusing on the optimization of multi-server configurations for profit maximization. The authors analyze the financial aspects of cloud computing, emphasizing the importance of understanding both service charges and business costs, which are influenced by application quality and multi-server system setup. The study considers the problem of optimal multi-server configuration in a cloud environment, proposing a valuation model that takes into account various factors such as service level agreements (SLAs), customer satisfaction, and cost considerations. The authors model a multi-server system as an M/M/m queuing model to facilitate logical analysis and optimization. Two server speed and power utilization models are considered: the idle-speed model and the constant-speed model. The probability density function of the waiting time for a service request is derived, and the expected service charge is calculated. The paper also reviews existing literature on cloud computing, including dynamic pricing strategies, optimal multi-server configurations, and the role of distributed computing in the IT industry. It discusses the challenges of market-oriented resource management in cloud computing, including the need for negotiation mechanisms, SLA compliance, and interoperability between cloud service providers. The authors also explore the use of utility theory in measuring customer satisfaction and the development of utility-based SLAs to balance application performance and operational costs. Additionally, the paper addresses power-aware scheduling techniques to reduce energy consumption in real-time systems and the use of dynamic voltage scaling to optimize power usage in cloud environments. The study concludes with a survey table and a discussion on the potential of a novel double-quality-guaranteed (DQG) renting scheme for service providers, which combines short-term and long-term renting to reduce resource waste and adapt to dynamic computing demands.This paper presents a survey on quality of service (QoS) in cloud computing, focusing on the optimization of multi-server configurations for profit maximization. The authors analyze the financial aspects of cloud computing, emphasizing the importance of understanding both service charges and business costs, which are influenced by application quality and multi-server system setup. The study considers the problem of optimal multi-server configuration in a cloud environment, proposing a valuation model that takes into account various factors such as service level agreements (SLAs), customer satisfaction, and cost considerations. The authors model a multi-server system as an M/M/m queuing model to facilitate logical analysis and optimization. Two server speed and power utilization models are considered: the idle-speed model and the constant-speed model. The probability density function of the waiting time for a service request is derived, and the expected service charge is calculated. The paper also reviews existing literature on cloud computing, including dynamic pricing strategies, optimal multi-server configurations, and the role of distributed computing in the IT industry. It discusses the challenges of market-oriented resource management in cloud computing, including the need for negotiation mechanisms, SLA compliance, and interoperability between cloud service providers. The authors also explore the use of utility theory in measuring customer satisfaction and the development of utility-based SLAs to balance application performance and operational costs. Additionally, the paper addresses power-aware scheduling techniques to reduce energy consumption in real-time systems and the use of dynamic voltage scaling to optimize power usage in cloud environments. The study concludes with a survey table and a discussion on the potential of a novel double-quality-guaranteed (DQG) renting scheme for service providers, which combines short-term and long-term renting to reduce resource waste and adapt to dynamic computing demands.