Read this article. Review the introduction to cloud computing and take notes on the aim and purpose of cloud computing. Then read the remainder of the article to become familiar with cloud-based services.
Keep in mind that cloud-based services can adapt or grow to meet the needs of the user. Service providers can deliver hardware and software based on organization needs. One benefit is that there is no need for organizations to own or allocate resources toward Information Technology (IT) staff.
A set of business-relevant key performance indicators (KPIs) that provide a standardised method to measure the business services are needed. The SMI framework provides a holistic view of the QoS needed by the customers for selecting a CSP based on accountability, agility, assurance of service, cost, performance, security and privacy, and usability. There are currently few publicly available metrics that define these KPIs and compare cloud providers. SMI is the first effort in this direction. The defined high-level attributes are accountability, agility, cost, performance, assurance, security and privacy, and usability. The proposed metrics for cloud KPI are service response time, sustainability, DCiE and PUE (the most prominent metrics used to measure the energy efficiency of a cloud computing service), suitability, accuracy, interoperability, availability, stability, adaptability, usability, throughput and efficiency, and scalability.
Li et al. proposed a metrics catalogue with multifold usability. This catalogue can be used as a dictionary to conveniently look up suitable metrics for cloud deployment. The existing evaluation metrics in the catalogue can help in developing metrics for research. Moreover, the preliminary metrics may help to better implement the evaluation of cloud services. Li et al. developed a set of tools to measure the metrics under four major providers: AWS, AppEngine, Azure, and cloud servers. The devised tool is simple and can be easily extended to other cloud platforms to measure application performance. In addition, Bojanova and Samba propose to determine the relative efficiencies of the different cloud computing models by measuring and analyzing the following cloud computing infrastructure metrics: hardware costs, software costs, and real-time provisioning costs. Real-time provisioning is integrated into the service management system by applying automated tools. Maiya et al. introduce cloud manageability metrics by defining the user's role, major use cases that the user performs, and metrics. Different platforms are used to validate this strategy. The proposed metrics are interfaces, documentation, time to learn, number of steps, time taken, and ease of use. Finally, propose the most relevant metrics and figures of merit for the evaluation of customer cloud benchmarks. These metrics are provisioning latency, provisioning throughput, and runtime performance, which are measured by latency, throughput, and bandwidth. The aforementioned metrics can be compared to the following attributes: scalability, stability, and reliability.