Are you on Mobile or Desktop? On the Impact of End-User Device on Web QoE Inference from Encrypted Traffic
| Autor: | Zied Ben Houidi, Hao Shi, Jinchun Xu, Sarah Wassermann, Nikolas Wehner, Michael Seufert, Joshua Schuler, Alexis Huet, Shengming Cai, Pedro Casas, Dario Rossi, Tobias Hossfeld |
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| Přispěvatelé: | Austrian Institute of Technology [Vienna] (AIT), Huawei, HUAWEI Technologies France (HUAWEI), University of Würzburg |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
02 engineering and technology Smartphone vs Desktop Encryption Machine Learning [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] [INFO.INFO-MC]Computer Science [cs]/Mobile Computing [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] 0202 electrical engineering electronic engineering information engineering Web navigation Quality of experience Network Monitoring business.industry Network packet End user [INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM] 020206 networking & telecommunications Network monitoring Web QoE SpeedIndex Key (cryptography) Encrypted Traffic 020201 artificial intelligence & image processing The Internet business Computer network |
| Zdroj: | 16th International Conference on Network and Service Management (CNSM) 16th International Conference on Network and Service Management (CNSM), Nov 2020, Izmir (virtual), Turkey CNSM |
| Popis: | International audience; 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 application-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. In this paper, we study 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 the most 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 to 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 the art, 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. |
| Databáze: | OpenAIRE |
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