Abstract
As video traffic dominates the Internet, it is important for operators to detect video quality of experience (QoE) to ensure adequate support for video traffic. With wide deployment of end-to-end encryption, traditional deep packet inspection--based traffic monitoring approaches are becoming ineffective. This poses a challenge for network operators to monitor user QoE and improve upon their experience. To resolve this issue, we develop and present a system for REal-time QUality of experience metric detection for <underline>E</underline>ncrypted Traffic—Requet—which is suitable for network middlebox deployment. Requet uses a detection algorithm that we develop to identify video and audio chunks from the IP headers of encrypted traffic. Features extracted from the chunk statistics are used as input to a machine learning algorithm to predict QoE metrics, specifically buffer warning (low buffer, high buffer), video state (buffer increase, buffer decay, steady, stall), and video resolution. We collect a large YouTube dataset consisting of diverse video assets delivered over various WiFi and LTE network conditions to evaluate the performance. We compare Requet with a baseline system based on previous work and show that Requet outperforms the baseline system in accuracy of predicting buffer low warning, video state, and video resolution by 1.12×, 1.53×, and 3.14×, respectively.
- Wireshark. n.d. About Wireshark. Retrieved May 15, 2020 from https://www.wireshark.org/about.html.Google Scholar
- Cisco. n.d. Cisco Annual Internet Report (2018--2023) White Paper. Retrieved May 15, 2020 from https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/mobile-white-paper-c11-520862.html.Google Scholar
- Telerik. n.d. Telerik Fiddler, the Free Web Debugging Proxy. Retrieved May 15, 2020 from https://www.telerik.com/fiddler.Google Scholar
- Fortune. 2016. How Google Is Making YouTube Safer For Its Users. Retrieved May 15, 2020 from http://fortune.com/2016/08/02/google-youtube-encryption-https/.Google Scholar
- Cisco. 2019. Cisco Encrypted Traffic Analytics. Retrieved May 15, 2020 from https://www.cisco.com/c/dam/en/us/solutions/collateral/enterprise-networks/enterprise-network-security/nb-09-encrytd-traf-anlytcs-wp-cte-en.pdf.Google Scholar
- 3GPP. 2010. Transparent End-to-End Packet-Switched Streaming Service (PSS). TS 26.234. 3rd Generation Partnership Project. 3GPP.Google Scholar
- Vaneet Aggarwal, Emir Halepovic, Jeffrey Pang, Shobha Venkataraman, and He Yan. 2014. Prometheus: Toward quality-of-experience estimation for mobile apps from passive network measurements. In Proceedings of ACM HotMobile.Google Scholar
Digital Library
- Adnan Ahmed, Zubair Shafiq, Harkeerat Bedi, and Amir R. Khakpour. 2017. Suffering from buffering? Detecting QoE impairments in live video streams. In Proceedings of IEEE ICNP.Google Scholar
- Johanna Amann, Oliver Gasser, Quirin Scheitle, Lexi Brent, Georg Carle, and Ralph Holz. 2017. Mission Accomplished? HTTPS Security AfterDigiNotar. In Proceedings of the ACM IMC Conference.Google Scholar
- Lucian Armasu. 2016. Netflix Adopts Efficient HTTPS Encryption for Its Video Streams. Retrieved May 15, 2020 from https://www.tomshardware.com/news/netflix-efficient-https-video-streams,32420.html.Google Scholar
- Francesco Bronzino, Paul Schmitt, Sara Ayoubi, Guilherme Martins, Renata Teixeira, and Nick Feamster. 2020. Inferring streaming video quality from encrypted traffic: Practical models and deployment experience. arXiv:1901.05800.Google Scholar
- Pedro Casas, Michael Seufert, and Raimund Schatz. 2013. YOUQMON: A system for on-line monitoring of YouTube QoE in operational 3G networks. SIGMETRICS Performance Evaluation Review 41, 2 (2013), 44--46.Google Scholar
Digital Library
- Giuseppe Cofano, Luca De Cicco, Thomas Zinner, Anh Nguyen-Ngoc, Phuoc Tran-Gia, and Saverio Mascolo. 2016. Design and experimental evaluation of network-assisted strategies for HTTP adaptive streaming. In Proceedings of ACM MMSys.Google Scholar
Digital Library
- Corinna Cortes and Vladimir Vapnik. 1995. Support-vector networks. Machine Learning 20, 3 (1995), 273--297.Google Scholar
Cross Ref
- Yong Cui, Tianxiang Li, Cong Liu, Xingwei Wang, and Mirja Kühlewind. 2017. Innovating transport with QUIC: Design approaches and research challenges. IEEE Internet Computing 21, 2 (2017), 72--76.Google Scholar
Digital Library
- Giorgos Dimopoulos, Ilias Leontiadis, Pere Barlet-Ros, and Konstantina Papagiannaki. 2016. Measuring video QoE from encrypted traffic. In Proceedings of ACM IMC.Google Scholar
Digital Library
- Florin Dobrian, Vyas Sekar, Asad Awan, Ion Stoica, Dilip Joseph, Aditya Ganjam, Jibin Zhan, and Hui Zhang. 2011. Understanding the impact of video quality on user engagement. In Proceedings of ACM SIGCOMM.Google Scholar
Digital Library
- Zakir Durumeric, Zane Ma, Drew Springall, Richard Barnes, Nick Sullivan, Elie Bursztein, Michael Bailey, J. Alex Halderman, and Vern Paxson. 2017. The security impact of HTTPS interception. In Proceedings of NDSS.Google Scholar
Cross Ref
- Roy T. Fielding and Julian F. Reschke. 2014. Hypertext Transfer Protocol (HTTP/1.1): Message Syntax and Routing. RFC 7230. IETF Trust.Google Scholar
- S. Galetto, P. Bottaro, C. Carrara, F. Secco, A. Guidolin, E. Targa, Claudio Narduzzi, and Giada Giorgi. 2017. Detection of video/audio streaming packet flows for non-intrusive QoS/QoE monitoring. In Proceedings of IEEE MN.Google Scholar
Cross Ref
- Thiago A. Guarnieri, Idilio Drago, Alex Borges Vieira, Ítalo Cunha, and Jussara M. Almeida. 2017. Characterizing QoE in large-scale live streaming. In Proceedings of IEEE GLOBECOM.Google Scholar
- Craig Gutterman, Katherine Guo, Sarthak Arora, Xiaoyang Wang, Les Wu, Ethan Katz-Bassett, and Gil Zussman. 2019. Requet: Real-time QoE detection for encrypted YouTube traffic. In Proceedings of of ACM MMSys.Google Scholar
Digital Library
- Tin Kam Ho. 1995. Random decision forests. In Proceedings of IEEE ICDAR.Google Scholar
- Te-Yuan Huang, Ramesh Johari, Nick McKeown, Matthew Trunnell, and Mark Watson. 2014. A buffer-based approach to rate adaptation: Evidence from a large video streaming service. In Proceedings of ACM SIGCOMM.Google Scholar
Digital Library
- Arash Molavi Kakhki, Samuel Jero, David R. Choffnes, Cristina Nita-Rotaru, and Alan Mislove. 2017. Taking a long look at QUIC: An approach for rigorous evaluation of rapidly evolving transport protocols. In Proceedings of ACM IMC.Google Scholar
Digital Library
- Vengatanathan Krishnamoorthi, Niklas Carlsson, Emir Halepovic, and Eric Petajan. 2017. BUFFEST: Predicting buffer conditions and real-time requirements of HTTP(S) adaptive streaming clients. In Proceedings of ACM MMSys.Google Scholar
Digital Library
- Will Law. 2018. Ultra-Low-Latency Streaming Using Chunked-Encoded and Chunked-Transferred CMAF. Technical Report. Akamai.Google Scholar
- Feng Li, Jae Won Chung, and Mark Claypool. 2018. Silhouette: Identifying YouTube video flows from encrypted traffic. In Proceedings of ACM NOSSDAV.Google Scholar
Digital Library
- Yu-Ting Lin, Eduardo Mucelli Rezende Oliveira, Sana Ben Jemaa, and Salah-Eddine Elayoubi. 2017. Machine learning for predicting QoE of video streaming in mobile networks. In Proceedings of IEEE ICC.Google Scholar
Cross Ref
- Sharat Chandra Madanapalli, Hassan Habibi Gharakheili, and Vijay Sivaraman. 2019. Inferring Netflix user experience from broadband network measurement. In Proceedings of IEEE TMA.Google Scholar
Cross Ref
- Tarun Mangla, Emir Halepovic, Mostafa Ammar, and Ellen Zegura. 2018. eMIMIC: Estimating HTTP-based video QoE metrics from encrypted network traffic. In Proceedings of IEEE TMA.Google Scholar
Cross Ref
- Tarun Mangla, Emir Halepovic, Mostafa H. Ammar, and Ellen W. Zegura. 2017. MIMIC: Using passive network measurements to estimate HTTP-based adaptive video QoE metrics. In Proceedings of IEEE TMA.Google Scholar
- Ahmed Mansy, Mostafa H. Ammar, Jaideep Chandrashekar, and Anmol Sheth. 2014. Characterizing client behavior of commercial mobile video streaming services. In Proceedings of ACM MoVid.Google Scholar
- Hongzi Mao, Ravi Netravali, and Mohammad Alizadeh. 2017. Neural adaptive video streaming with Pensieve. In Proceedings of ACM SIGCOMM.Google Scholar
Digital Library
- M. Hammad Mazhar and Zubair Shafiq. 2018. Real-time video quality of experience monitoring for HTTPS and QUIC. In Proceedings of IEEE INFOCOM.Google Scholar
- Abhijit Mondal, Satadal Sengupta, Bachu Rikith Reddy, M. J. V. Koundinya, Chander Govindarajan, Pradipta De, Niloy Ganguly, and Sandip Chakraborty. 2017. Candid with YouTube: Adaptive streaming behavior and implications on data consumption. In Proceedings of NOSSDAV.Google Scholar
Digital Library
- Irena Orsolic, Dario Pevec, Mirko Suznjevic, and Lea Skorin-Kapov. 2016. YouTube QoE estimation based on the analysis of encrypted network traffic using machine learning. In Proceedings of IEEE Globecom Workshops.Google Scholar
Cross Ref
- Irena Orsolic, Mirko Suznjevic, and Lea Skorin-Kapov. 2018. YouTube QoE estimation from encrypted traffic: Comparison of test methodologies and machine learning based models. In Proceedings of QoMEX. IEEE, Los Alamitos, CA, 1--6.Google Scholar
Cross Ref
- Stefano Petrangeli, Tingyao Wu, Tim Wauters, Rafael Huysegems, Tom Bostoen, and Filip De Turck. 2017. A machine learning-based framework for preventing video freezes in HTTP adaptive streaming. Journal of Network and Computer Applications 94 (2017), 78--92.Google Scholar
Digital Library
- Abbas Razaghpanah, Arian Akhavan Niaki, Narseo Vallina-Rodriguez, Srikanth Sundaresan, Johanna Amann, and Phillipa Gill. 2017. Studying TLS usage in Android apps. In Proceedings of ACM CoNEXT.Google Scholar
Digital Library
- Andrew Reed and Michael Kranch. 2017. Identifying HTTPS-protected Netflix videos in real-time. In Proceedings of CODASPY.Google Scholar
Digital Library
- Paul Schmitt, Francesco Bronzino, Renata Teixeira, Tithi Chattopadhyay, and Nick Feamster. 2018. Enhancing transparency: Internet video quality inference from network traffic. In Proceedings of TPRC46.Google Scholar
Cross Ref
- Susanna Schwarzmann, Clarissa Cassales Marquezan, Marcin Bosk, Huiran Liu, Riccardo Trivisonno, and Thomas Zinner. 2019. Estimating video streaming QoE in the 5G architecture using machine learning. In Proceedings of the ACM MobiCom Internet-QoE Workshop.Google Scholar
Digital Library
- Michael Seufert, Pedro Casas, Nikolas Wehner, Li Gang, and Kuang Li. 2019. Features that matter: Feature selection for on-line stalling prediction in encrypted video streaming. In Proceedings of IEEE INFOCOM Network Intelligence: Machine Learning for Networking Workshop.Google Scholar
Cross Ref
- Thomas Stockhammer. 2011. Dynamic adaptive streaming over HTTP: Standards and design principles. In Proceedings of ACM MMSys.Google Scholar
Digital Library
- Dimitrios Tsilimantos, Theodoros Karagkioules, and Stefan Valentin. 2018. Classifying flows and buffer state for YouTube’s HTTP adaptive streaming service in mobile networks. arXiv:1803.00303.Google Scholar
- Dimitrios Tsilimantos, Theodoros Karagkioules, and Stefan Valentin. 2018. Classifying flows and buffer state for YouTube’s HTTP adaptive streaming service in mobile networks. In Proceedings of ACM MMSys.Google Scholar
Digital Library
- Vladislav Vasilev, Jérémie Leguay, Stefano Paris, Lorenzo Maggi, and Mérouane Debbah. 2018. Predicting QoE factors with machine learning. In Proceedings of IEEE ICC.Google Scholar
Cross Ref
- Nick Vogt. 2015. YouTube Audio Quality Bitrate Used for 360p, 480p, 720p, 1080p, 1440p, 2160p. Retrieved May 15, 2020 from https://www.h3xed.com/web-and-internet/youtube-audio-quality-bitrate-240p-360p-480p-720p-1080p.Google Scholar
- Florian Wamser, Michael Seufert, Pedro Casas, Ralf Irmer, Phuoc Tran-Gia, and Raimund Schatz. 2015. YoMoApp: A tool for analyzing QoE of YouTube HTTP adaptive streaming in mobile networks. In Proceedings of EuCNC.Google Scholar
Cross Ref
- Sarah Wassermann, Michael Seufert, Pedro Casas, Li Gang, and Kuang Li. 2019. I see what you see: Real time prediction of video quality from encrypted streaming traffic. In Proceedings of the ACM MobiCom Internet-QoE Workshop.Google Scholar
Digital Library
- Nicolas Weil. n.d. The State of MPEG-DASH 2016. Retrieved May 15, 2020 from http://www.streamingmedia.com/Articles/Articles/Editorial/Featured-Articles/The-State-of-MPEG-DASH-2016-110099.aspx.Google Scholar
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Requet: Real-Time QoE Metric Detection for Encrypted YouTube Traffic
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