Visible to the public QoE Inference Without Application Control

TitleQoE Inference Without Application Control
Publication TypeConference Paper
Year of Publication2016
AuthorsNikravesh, Ashkan, Hong, David Ke, Chen, Qi Alfred, Madhyastha, Harsha V., Mao, Z. Morley
Conference NameProceedings of the 2016 Workshop on QoE-based Analysis and Management of Data Communication Networks
PublisherACM
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-4425-8
KeywordsDiagnosis, forward error correction, Forward Error Correction and Security, Measurement, pubcrawl, qoe, Resiliency, Scalability, security, Traffic Management, video
AbstractNetwork quality-of-service (QoS) does not always directly translate to users' quality-of-experience (QoE), e.g., changes in a video streaming app's frame rate in reaction to changes in packet loss rate depend on various factors such as the adaptation strategy used by the app and the app's use of forward error correction (FEC) codes. Therefore, knowledge of user QoE is desirable in several scenarios that have traditionally operated on QoS information. Examples include traffic management by ISPs and resource allocation by the operating system (OS). However, today, entities such as ISPs and OSes that implement these optimizations typically do not have a convenient way of obtaining input from applications on user QoE. To address this problem, we propose offline generation of per-application models mapping application-independent QoS metrics to corresponding application-specific QoE metrics, thereby enabling entities (such as ISPs and OSes) that can observe a user's network traffic to infer the user's QoE, in the absence of direct input. In this paper, we describe how such models can be generated and present our results from two popular video applications with significantly different QoE metrics. We also showcase the use of these models for ISPs to perform QoE-aware traffic management and for the OS to offer an efficient QoE diagnosis service.
URLhttp://doi.acm.org/2940136.2940145
DOI2940136.2940145
Citation Keynikravesh_qoe_2016