# Survey on Quality of Experience Evaluation for Cloud-Based Interactive Applications
## 1 Introduction
For example, an increase of 500 ms in interactivity time for interactive web services can result in significant costs and reduced user activity [12]. The growing deployment of cloud services, especially those with interactivity requirements, highlights the need to quantify and monitor the QoE. The COVID-19 pandemic has enforced measures in favour of public health, requiring the facilitation of remote work for a significant portion of the workforce. Many employers and employees, who had not previously participated in remote work arrangements, have transitioned to this new model. Studies in the literature [13] have suggested that approximately 40% of large and small companies expect that 40% or more of their workers who switched to remote work would continue to do so even after the health crisis. These estimates indicate that at least 16% of workers will perform their jobs from home at least two days a week as a result of the COVID-19 pandemic. A significant number of services enabling remote work fall within the domain of CIAs [14; 15; 16]. Ranging from office automation to remote desktops, these services are increasingly prevalent and require the development of precise ad hoc monitoring to ensure the necessary QoE [17]. Remote employees relying on services like remote desktops benefit from a seamless experience and good interactivity, resulting in fewer complaints for the support centre and allowing end users to concentrate on their tasks. Companies are actively working to guarantee a high QoE for these services, ultimately ensuring the productivity and satisfaction of their remote employees [18; 19]. Given this scenario, it is essential to measure the QoE of CIA users in deployments that are increasingly complex. In this work, we conduct an evaluation of existing state-of-the-art proposals for assessing QoE in CIAs. We describe the analytical tools and identify three stages common to most proposals in the state of the art: input, processing, and output. We also compile the metrics used by the tools and describe how they are transformed throughout these three stages. Our study makes a significant contribution by focusing on strategies for monitoring QoE in CIAs and proposing improvements. To the best of our knowledge, there are no surveys on QoE in CIAs, although related survey papers are presented in the relevant sections. After identifying the challenge of ensuring productivity in CIAs in the post-COVID-19 context, it becomes evident that studying existing proposals for QoE assessment is crucial. We analyse over 28 proposals, identifying key aspects of human perception. Additionally, we explore the architecture and operation of CIAs, distinguishing them from other applications in the non-interactive cloud. We classify the proposals into five main categories based on their strategies and group metrics. Finally, we dedicate a specific section to open issues, where we discuss possible avenues for improving QoE measurement in CIAs. The remainder of this paper is structured as shown in Figure 1. Section 2 reviews related works and addresses human perceptions of users and their influence on the CIA's QoE. Section 3 presents the categories of CIAs and how the state-of-the-art proposals focus on each of them. Section 4 details the characteristics of the CIA's infrastructure and the different location possibilities for QoE monitoring tools. Section 5 groups the literature proposals into the main strategies used to measure QoE in CIAs. In this section, we detail how each proposal derives its measure of QoE. Section 6 outlines the three stages these strategies must go through for QoE assessment. Finally, Section 7 explores open issues, and Section 8 concludes this paper.