Threshold-oblivious on-line web QoE assessment using neural network-based regression model

The evaluation of the web-browsing quality of experience (QoE) is difficult to complete through traditional methods (e.g. deducing formulas or setting thresholds) due to the diversity of websites and their contents. To evaluate web-browsing QoE through a general way, the authors propose a web QoE evaluation architecture based on machine learning, consisting of two parts: traffic classification sub-system and QoE prediction sub-system. When evaluating user experience, traffic classification sub-system first classifies the packets generated by visiting a website into a flowthrough some fields in the packet header, to model each website separately. The traffic classification accuracy of > 2000 packets over six websites reaches 96.63%. Then, in the network layer, the traffic metric cumulative traffic volume is generated from the size and arrival time of packets. When a user visits a web page, their regression model predicts the above-the-fold time (ATF) and thus QoE. The output of the regression model is an exact ATF value that is mapped to user experience. In addition, reversing input variables further improves the model, which is evaluated on two popular websites. The QoE prediction results of the improved method for 5400 visits are obtained within 0.0975 s, reaching 0.9 R2 score.