%0 Conference Paper %1 info3-inproceedings-2020-10 %A Wassermann, Sarah %A Casas, Pedro %A Ben Houidi, Zied %A Huet, Alexis %A Seufert, Michael %A Wehner, Nikolas %A Schüler, Joshua %A Cai, Sheng-Ming %A Shi, Hao %A Xu, Jinchun %A Hoßfeld, Tobias %A Rossi, Dario %B 16th International Conference on Network and Service Management (CNSM) %C Izmir, Turkey %D 2020 %K myown ucn %T Are you on Mobile or Desktop? On the Impact of End-User Device on Web QoE Inference from Encrypted Traffic %X Web browsing is one of the key applications of the Internet, if not the most important one. We address the problem of Web Quality of Experience (QoE) monitoring from the ISP perspective, relying on in-network, passive measurements. As a proxy to Web QoE, we focus on the analysis of the well-known SpeedIndex (SI) metric. Given the lack of app-level data visibility introduced by the wide adoption of end-to-end encryption, we resort to machine-learning models to infer the SI and the QoE level of individual web page loading sessions, using as input only packet- and flow-level data. Our study targets the impact of different end-user device types (e.g., smartphone, desktop, tablet) on the performance of such models. Empirical evaluations on a large, multi-device, heterogeneous corpus of Web QoE measurements for top popular websites demonstrate that the proposed solution can infer the SI as well as estimate QoE ranges with high accuracy, using either packet-level or flow-level measurements. In addition, we show that the device type adds a strong bias in the feasibility of these Web QoE models, putting into question the applicability of previously conceived approaches on single-device measurements. To improve the state of affairs, we conceive cross-device generalizable models operating at both packet and flow levels, offering a feasible solution for Web QoE monitoring in operational, multi-device networks. To the best of our knowledge, this is the first study tackling the analysis of Web QoE from encrypted network traffic in multi-device scenarios.

@inproceedings{info3-inproceedings-2020-10, abstract = {Web browsing is one of the key applications of the Internet, if not the most important one. We address the problem of Web Quality of Experience (QoE) monitoring from the ISP perspective, relying on in-network, passive measurements. As a proxy to Web QoE, we focus on the analysis of the well-known SpeedIndex (SI) metric. Given the lack of app-level data visibility introduced by the wide adoption of end-to-end encryption, we resort to machine-learning models to infer the SI and the QoE level of individual web page loading sessions, using as input only packet- and flow-level data. Our study targets the impact of different end-user device types (e.g., smartphone, desktop, tablet) on the performance of such models. Empirical evaluations on a large, multi-device, heterogeneous corpus of Web QoE measurements for top popular websites demonstrate that the proposed solution can infer the SI as well as estimate QoE ranges with high accuracy, using either packet-level or flow-level measurements. In addition, we show that the device type adds a strong bias in the feasibility of these Web QoE models, putting into question the applicability of previously conceived approaches on single-device measurements. To improve the state of affairs, we conceive cross-device generalizable models operating at both packet and flow levels, offering a feasible solution for Web QoE monitoring in operational, multi-device networks. To the best of our knowledge, this is the first study tackling the analysis of Web QoE from encrypted network traffic in multi-device scenarios.}, added-at = {2020-09-15T11:39:51.000+0200}, address = {Izmir, Turkey}, author = {Wassermann, Sarah and Casas, Pedro and Ben Houidi, Zied and Huet, Alexis and Seufert, Michael and Wehner, Nikolas and Schüler, Joshua and Cai, Sheng-Ming and Shi, Hao and Xu, Jinchun and Hoßfeld, Tobias and Rossi, Dario}, biburl = {https://www.bibsonomy.org/bibtex/23b7811b4fff206a8893de754141850ec/uniwue_info3}, booktitle = {16th International Conference on Network and Service Management (CNSM)}, interhash = {09bdfe9a01be7342a1239107573c4251}, intrahash = {3b7811b4fff206a8893de754141850ec}, keywords = {myown ucn}, month = {11}, timestamp = {2022-03-14T00:08:42.000+0100}, title = {Are you on Mobile or Desktop? On the Impact of End-User Device on Web QoE Inference from Encrypted Traffic}, year = 2020 }