# An Experimental Performance Evaluation of Cloud-API-Based Applications
## 2 Related Work  
Several studies have assessed and evaluated cloud computing applications by considering several techniques [6; 7; 8]. Researchers have investigated the capacity of cloud servers and the performance of the applications uploaded to these cloud environments. [PERSON] et al. [6] presented an efficient assessment study that compared HPC and the Amazon AWS EC2 cloud platform. The experiments employed real applications that represented the workload of a super-computing center. From the experimental results, it was shown that using a Linux cluster was six-times faster than Amazon AWS EC2, and modern HPC systems were found to be twenty-times faster than Amazon AWS EC2. The performance of the tested applications was limited by interconnecting with the Amazon AWS EC2 platform. Moreover, the Amazon AWS EC2 platform introduces substantial variability. Moreover, [PERSON] et al. [7] used \"ISO Quality Characteristics\" to evaluate the performance of the applications uploaded to the cloud computing environment. The main consideration regarding this experimental study was the high complexity of the cloud environment, which is directly inherited from the complex infrastructure. In this experimental study, they adopted a new measurement framework and applied it to log data obtained from the data center. This was performed to map and examine particular quality characteristics of the ISO (i.e., the behavior during a period of time). A particular industrial private cloud was used to conduct this experimental study. The experimental results confirmed the effectiveness of the proposed framework at measuring and evaluating the performance of the uploaded applications. [PERSON] et al. [PERSON] et al. (2018) investigated the performance of big data applications, which combine the concepts of software quality from the ISO25010 model. Their experimental study aimed to fill the gap in the numerical representation of these concepts and the measurement of big data applications' performance. New models and frameworks have been proposed, aiming to facilitate the experimental study of applications uploaded to the cloud environment. [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) presented a new model to analyze the performance of web applications in the cloud computing environment. They modeled the IaaS platform as multiple queues and \"Virtual Machines\" (VMs) as service centers. This model depends on the application behavior of the reverse instances on Amazon EC2. On the other hand, [PERSON] et al. [PERSON] et al. (2018) proposed a new framework that combines different monitoring and benchmarking tools. Performance engineers can use this framework to test the applications under different loads and configurations. Moreover, the dynamic allocation of the sever is supported through this framework depending on the incoming load by employing response-time-aware heuristics. Many researchers have investigated different performance metrics of the cloud environment for several applications. An experimental assessment of the performance of an analytic application that is run through different configurations of the storage service was presented in [PERSON] and [PERSON] (2018). The authors analyzed the performance achieved through analytic workloads and explained the problems that occurred due to impedance mismatch, which occurs in some configurations. [PERSON] et al. [PERSON] et al. (2018) tried to help the users choose the optimal configuration of each application by creating a prediction model using the cloud providers for the offered system. This model of prediction was created by applying machine-learning methods. They used the OpenStack cloud system to ensure the validity of the suggested methodology. [PERSON] et al. measured the performance of the most commonly used cloud platforms (i.e., Amazon AWS EC2 and Microsoft Azure). The experiments were run by generating clusters of Azure and AWS instances. Then, they executed the benchmarks related to the several instances.