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Characterizing Microsoft Lync Online in Mobile Networks: a Quality of Experience Perspective

TitleCharacterizing Microsoft Lync Online in Mobile Networks: a Quality of Experience Perspective
Publication TypeConference Paper
Year of Publication2014
AuthorsCasas, P., A. Sackl, S. Egger, and R. Schatz
Conference Name3rd IEEE International Conference on Cloud Networking
Date Published10/2014
Conference LocationLuxembourg, Luxembourg
KeywordsAudioconferencing, Cloud QoE, Distributed Data Center, Microsoft Lync Online, MOS, Remote Desktop Sharing, Telepresence, Videoconferencing

Cloud-based systems are gaining enormous popularity due to a number of promised benefits, including ease of deployment and administration, scalability and flexibility, and costs savings. However, as more personal and business applications migrate to the Cloud, the service quality becomes an important differentiator between providers. ISPs, Cloud providers and enterprises migrating their services to the Cloud must therefore understand the network requirements to ensure proper end-user Quality of Experience (QoE) in these services. This paper addresses the problem of QoE in Telepresence and Remote Collaboration (TRC) services provided by Microsoft Lync Online (MLO). MLO is a Cloud-based service providing online meeting capabilities including videoconferencing, audio calls, and desktop sharing, and has become the default system for TRC in enterprise scenarios. We present a complete study of the QoE undergone by 44 MLO users in controlled subjective lab tests. The study is performed on three different interactive scenarios running on top of the real MLO Cloud service, additionally shaping the Lync flows at the access network to influence the participants experience. The scenarios include audioconferencing, videoconferencing, and remote collaboration though desktop sharing. By passively monitoring the end-to-end QoS achieved by the Lync flows, and correlating it with the QoE feedbacks provided by the participants, this study permits to better understand the interplays between network performance and QoE in TRC Cloud services. In addition, we provide a network-level characterization of the traffic generated by MLO, as well as an overview on the infrastructure hosting MLO servers.

Citation KeyCas2014e
Refereed DesignationRefereed
Project year: 
Second year
WP(s) associated with the paper: 
WP4 - mPlane Supervisor: Iterative and Adaptive Analysis
Partner(s) associated with the paper's author(s): 
Forschungszentrum Telekommunikation Wien Gmbh
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