With the rapid expansion of cloud services and the increasing complexity of its various layers, including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), monitoring and evaluating the performance of these services has become a significant challenge. This paper introduces a novel architecture for cloud service monitoring, leveraging multi-layer service monitoring through index extraction and performance analysis. The proposed architecture, utilizing a hybrid approach of data extraction and index analysis, can simulate the performance status, scalability, and service quality at each layer. This approach enables cloud service administrators to identify performance issues, potential threats, and scalability deficiencies, thereby effectively improving service quality. The proposed architecture specifically addresses scalability and performance requirements in large-scale cloud environments. Furthermore, this paper discusses the potential challenges and barriers in implementing large-scale monitoring architectures and provides practical and actionable solutions to tackle these issues within the proposed architecture. Additionally, a comparison with existing traditional architectures reveals that the proposed architecture is significantly superior, particularly in terms of security, scalability, integrity, and adaptability. Finally, this study highlights the importance of future research on the impact of distributed and decentralized storage systems, such as Blockchain technology, on the security and scalability of data warehouses in global cloud monitoring environments.