# Survey on Quality of Experience Evaluation for Cloud-Based Interactive Applications
## 7 Open Issues and Lessons Learned  
Cloud-based interactive applications and services are becoming increasingly prevalent. The pandemic situation caused by COVID-19 has strongly accelerated the deployment of some of these solutions, particularly remote desktop services. The general public is increasingly using solutions with high interactivity requirements. This trend must be accompanied by efforts to assess the QoE that CIAs offer. Service administrators need to ensure good QoE so that user productivity is sustained. We observed that over the last three years, the number of research studies addressing the evaluation of QoE in CIAs has increased. We hope that given the current situation, further progress will be made in this field. Presently, researchers need to collect input metrics for their proposals from CIA infrastructure elements. There is a need for CIA developers to provide specific APIs and software for assessing the performance of CIAs deployed in public or private cloud environments. This would streamline the work of service administrators and allow them to adjust the computational resources of the CIA architecture in real time. In addition, communication protocols are, in most cases, proprietary. This makes it difficult to develop methodologies that can be generalised to more than one CIA. In view of this fact, methodologies such as slow-motion benchmarking require instrumenting clients and modifying user behaviour to extract QoE measurements. Therefore, the measurements may not be representative. In addition, many of the proposals we evaluated have a particularised approach for specific CIAs. Hence, it is necessary to develop new and more general strategies. A number of proposals bundle QoS metrics and offer them as a new measure of QoE quantification. Authors such as [PERSON] et al. [36] and [PERSON] et al. [31] studied the correlation between their input metrics and the user's final perception. To take the step of relating QoS to QoE, the studies conducted laboratory tests with a large number of users who experienced different boundary conditions while answering questions about their usage experience. However, a large number of the proposals we analysed did not demonstrate any direct relationship between their QoS metrics and the QoE evaluation of the CIA. This is the main shortcoming we identified. This missing step is costly and time-consuming due to the need for laboratory tests, but it will enable one to obtain truly representative QoE metrics. To overcome this deficiency, it is necessary to conduct controlled lab experiments [101]. The procedure has been standardised and photocolised by ITU-T [102; 103; 104]. According to these standards, it is necessary to control the conditions of the overall evaluation process. The experimenter must control the content and context of the process and inform and observe the user at any moment during the lab tests. However, other possibilities are becoming more and more common when it comes to assessing the performance of networks and services from a QoE end-user perspective. Some authors understand that the user experience is also influenced by factors such as the context of use, the preferences of some services over others, or the device a user is used to [85]. This is why another possibility is to conduct controlled experiments in the user's own environment. These experiments would yield more realistic results that complement those carried out in the laboratory [105], even though they complicate the procedure by having to be conducted in the user's environment. Most proposals lack an assessment of the performance of the measurement system, including its impact on the systems it operates on. Only a few studies propose methodologies, such as instrumenting elements of the CIA architecture with reduced computational resources (e.g., thin clients), but it is not common for researchers to thoroughly evaluate the potential impact on CPU, RAM, or disk usage.