# Survey on Quality of Experience Evaluation for Cloud-Based Interactive Applications
## 5 Strategies for QoE Measurement in CIAs
### Strategies Based on Indirect Measures  
This strategy refers to proposals that quantify QoE through indirect measures. Indirect measures are those obtained from other intermediate measures that, in principle, are not directly related to QoE. We explain below the most common intermediate measures used by researchers to assess QoE. In [47], [PERSON] et al. proposed an alternative to avoid the instrumentation of interactive web applications and obtain the SI without incurring high computational costs. Although the SI traditionally requires the instrumentation of the CIA, the authors suggested obtaining the metric through indirect measurements. Their proposal used network traffic and artificial intelligence models. To train the models, [PERSON] et al. extracted the number of bytes, number of packets, arrival times, and bandwidth generated by the CIA. From these values, they calculated statistics such as the mean, maximum value, and sum. In the study, they tested different models: k-nearest neighbours (KNN), decision trees, random forests, extreme gradient descent (XGB), LSTM neural networks, and gated recurrent units (GRUs). The LSTM networks provided the best results for estimating the SI from network traffic in interactive web applications. In [48], [PERSON] et al. defined the relationship between different main key performance indicators (KPIs) of the indirect measures that are relevant to determining the QoE in cloud gaming CIAs. However, the QoE model resulting from the experiments was a conceptualisation and did not provide a final definition specifying which KPI transformations are necessary to obtain a real QoE value. [PERSON] et al. [41] proposed latency as an indirect measure. The authors aimed to evaluate the QoE in remote desktop CIAs by employing active traffic measurements. The study suggested using the response time of ICMP (ping) requests from the client to the cloud server. The approach requires creating a baseline by pinging a cloud server. Following this previous work, [PERSON] et al. used this baseline to evaluate the QoE in CIAs in real scenarios. When the server is more loaded or network administrators add new virtual machines to the server, some ping requests will show higher times. Their indirect latency measure assesses these anomalous high values that deviate from the norm. However, again, the paper did not clarify the relationship between the indirect measure and QoE. Furthermore, it did not use real users to evaluate whether there was a real relationship between the peak latency phenomenon and a decrease in QoE. Some proposals offer proprietary measures of QoS as proxy measures for QoE quantification. The most common ones are CPU, GPU, RAM, disk, and network usage. These metrics are traditionally used to evaluate equipment performance but are not necessarily directly related to the user experience. Some authors obtained these metrics from the client or the cloud server. In [31], [PERSON] et al. used network metrics to evaluate QoE in remote desktop and interactive web page CIAs. In their study, the authors analysed the QoE in different commercial CIAs. They subjected the service to different boundary conditions and obtained network metrics such as upstream bandwidth, downstream bandwidth, and RTT. The authors attempted to map these metrics to QoE using laboratory tests with real users. In these tests, the researchers also calculated the synthetic interactivity time, which can be derived from network measurements, such as the RTT. However, the study did not provide much detail about how to obtain the synthetic interactivity time or its measurement error. If CPU, GPU, RAM, or disk usage is high, the different tasks running on a computer may be queued, and the user may perceive a slowdown in the CIA services consumed. When there are no processes running on the computers, the use of computing resources remains stable. A significant variation in the stability of the usage of these measures may be indicative of the start of new processes on the computers in the CIA architecture. [PERSON] et al.