Enabling Data-Driven Optimization of Quality of Experience for Internet Applications

Tuesday, January 24, 2017

4:00 PM5:00 PM

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The School of Informatics and Computing (SoIC) Computer Science (CS) Colloquium Series

Speaker:   Junchen Jiang, Carnegie Mellon University

Where:  State Room East, Indiana Memorial Union

When:  Tuesday January 24, 2017 04:00 PM

Topic: Enabling Data-Driven Optimization of Quality of Experience for Internet Applications

Abstract:  Today’s Internet has become an “eyeball economy” dominated by applications such as video streaming (e.g., Netflix counts for 37% of Internet traffic) and VoIP (e.g., Skype users spend over 2 billion minutes talking to each other every day). With most applications relying on user engagement to generate revenues, maintaining high user-perceived QoE (Quality of Experience) is crucial to ensure high user engagement. For instance, one short buffering interruption could lead to 39% less time spent watching videos and causes significant revenue losses for ad-based video sites. Despite increasing expectations for high QoE, existing approaches have limitations to achieve the QoE needed by today’s applications. They either require costly re-architecting of the network core, or use suboptimal endpoint-based protocols to react to the dynamic Internet performance based on limited knowledge of the network.

In this talk, I will present a new approach, inspired by the recent success of data-driven approaches in many fields of computing. I will demonstrate that data-driven techniques can improve Internet QoE by utilizing a centralized real-time view of performance across millions of endpoints (clients), rather than per-endpoint information. I will focus on two fundamental challenges that are unique to applying data-driven approaches in networking: the need for expressive models to capture complex factors affecting QoE, and the need for scalable platforms to make real-time decisions with fresh data from geo-distributed clients. I address these challenges in practice by integrating several domain-specific insights in networked applications with machine learning algorithms and systems, and achieve better QoE than using off-the-shelf machine learning solutions. I will present end-to-end systems that yield substantial QoE improvement for video streaming and VoIP, which could lead to to higher user engagement and revenue increase for application providers. Two of my projects, CFA and VIA, have been used in industry by Conviva and Skype, companies that specialize in QoE optimization for video streaming and VoIP, respectively.

Biography:  Junchen Jiang is a PhD candidate at Carnegie Mellon University, advised by Dr. Vyas Sekar and Dr. Hui Zhang . His research interests are computer networks, big data, and cloud computing. His dissertation applies big data techniques to optimizing Quality of Experience of Internet applications, including Internet video and Internet telephony. Many papers resulting from his research have been published in top venues, including ACM SIGCOMM and USENIX NSDI. Junchen received his bachelor's degree in computer science from Tsinghua University, China, in 2011. He received Juniper Networks Fellowship, and has won a paper award from ACM CoNEXT 2012.