att_abstract={{Cellular network operators are expected to maintain a good quality-of-experience (QoE) for many services beyond circuit-switched voice and messaging. However, new smart-phone ``app'' services, such as over-the-top video delivery and real-time video calling, are not under an operator's control. Furthermore, operators cannot practically deploy active probes to monitor app QoE in all the places its subscribers may be and complex interactions between network protocol layers make it challenging for operators to understand how network-level parameters and performance will influence an app's QoE. This paper takes a first step to address these challenges by presenting a novel approach to estimate app QoE using passive network measurements. Our approach uses machine learning to obtain a function that relates passive measurements to an app's QoE. In contrast to previous approaches, our approach does not require any control over app services or domain knowledge about how an app's network traffic relates to QoE. We implemented our approach in Prometheus, a prototype system in a large U.S. cellular operator. We show that Prometheus can track the QoE of real video-on-demand and VoIP apps with over 80% accuracy, which is at least as accurate as approaches suggested by domain experts.}},
	att_authors={va037f, eh679n, jp935w, sv1623, hy312y},
	att_copyright_notice={{(c) ACM, 2014. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in 2014 {{, 2014-02-26}}.
	author={Vaneet Aggarwal and Emir Halepovic and Jeffrey Pang and Shobha Venkataraman and He Yan},
	institution={{ACM HotMobile 2014}},
	title={{Prometheus: Toward Quality-of-Experience Estimation for Mobile Apps from Passive Network Measurements}},