Abstract
Nowadays, more and more online content providers are offering multiple types of data services. To provide users with a better service experience, Quality of Experience (QoE) has been widely used in the delivery quality measurement of network services. How to accurately measure the QoE score for all types of network services has become a meaningful but difficult problem. To solve this problem, we proposed a unified QoE scoring framework that measures the user experience of almost all types of network services. The framework first uses a machine learning model (random forest) to classify network services, then selects different nonlinear expressions based on the type of service and comprehensively calculates the QoE score through the Quality of Service (QOS) metrics including transmission delay, packet loss rate, and throughput rate. Experiment results show that the proposed method has the ability to be applied on almost all the types of network traffic, and it achieves better QoE assessment accuracy than other works.
Highlights
With the growing development of communication and multimedia technology, more and more people use the Internet to obtain information online, such as browsing the web, listening to music, watching videos, downloading files, and so on
We proposed a network traffic classification-based nonlinear mathematical Quality of Experience (QoE) quantization model for universal traffic types, which can be seen as a quality of service (QoS)-based QoE quantization method
We compare the classification performance between our applied random forest and other popular classifiers, e.g., support vector machine (SVM), feedforward neural network (NN), and top-K nearest neighbors; the results shown in Figure 3 suggest that the random forest classifier outperforms the other classifiers in Precision, Recall Rate, and F1-Score
Summary
With the growing development of communication and multimedia technology, more and more people use the Internet to obtain information online, such as browsing the web, listening to music, watching videos, downloading files, and so on. It has been demonstrated that the QoS primarily targets at improving the service quality with respect to application-level technical parameters, which lacks sufficient consideration of the user’s actual perceptions and feelings [1] To solve this problem, the user-centric measurement of service quality with the notion of quality of experience (QoE) is introduced, which has drawn much attention in both academia and industry [2,3,4]. The assessment of QoE can be done in both subjective and objective ways [5], where subjective evaluation is usually implemented by questionnaires and rating scales [6], can be treated as the more direct and reliable way to evaluate QoE scores It is time-consuming, costly, and inconvenient [7]. Another assessment appraoch of QoE, objective assessment, uses predefined models to approximate the subjective QoE estimation without human involvement [5]
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