Lehrstuhl für Informatik III

    mPlane - Building an Intelligent Measurement Plane for the Internet


    Phuoc Tran-Gia

    Florian Wamser

    Matthias Hirth


    Valentin Burger

    Christian Schwartz


    University of Würzburg is involved in the following mPlane work packages.

    • WP2 - Programmable Probes: Crowdsourcing based network measurements of selected cloud applications.
    • WP3 - Large-scale data analysis: Analysis of measurements traces from cloud applications on network layer and application layer.

    The joint collaboration successfully led to the following joint publications with other mPlane project partners.

    Crowdsourcing based network measurements [1]:

    Several approaches exist for network measurements ranging from analysing live traffic traces from campus or Internet Service Providers (ISPs) networks, to performing active measurements on distributed testbeds like PlanetLab, or involving volunteers. Each method falls short in offering only a partial vision. For instance, the scope of traffic traces is limited to the ISP's network and customers' habits, while active measurements might be biased by the population or node location involved in the experiment. To complement these techniques we propose to use (commercial) crowdsourcing platforms for network measurements. They permit a controllable, diverse and realistic view on the Internet and provide better control than measurements with voluntary participants. In [1], we compare crowdsourcing with traditional measurement techniques, describe possible pitfalls and limitations and present best practices to overcome these issues. The major contribution of this paper comprises a guideline for researchers when and how to exploit crowdsourcing for network measurements.

    On QoE for File Storage Services [2]:

    Cloud computing is receiving growing attention by researchers from a variety of disciplines. However, so far only a few studies exist that investigate th eQoE of cloud services, including the category of personal file storage services like Dropbox, from an end user perspective. In [2], a methodology and the results of four different user studies towards a situational QoE model for file storage services are provided. In order to obtain insights in existing usage practices and to detect possible QoE influencing factors and relevant features, we conducted an online survey amongst users of personal cloud storage services in general and one specifically targeted at users of one of the major popular cloud storage services, namely Dropbox. A third study on mobile Dropbox further investigated QoE and specific use cases in a mobile context, via smartphones and tablets. Based on these results, typical usage situations as well as short-term and long-term QoE influence factors were derived which included user profile, context and situation, as well as system level influences. In a fourth study the impact of waiting times as short-term QoE influence on system level during the regular usage of Dropbox is investigated. As an outcome of our studies, we formulate research questions driving the agenda towards measuring and modeling QoE for cloud-based file storage services.

    QoE Model for YouTube and Different Transport Protocols [3]:

    Video streaming currently dominates global Internet traffic and will be of even increasing importance in the future. In [3], we assess the impact of the underlying transport protocol on the user perceived quality for video streaming using YouTube as example. In particular, we investigate whether UDP or TCP fits better for Video-on-Demand delivery from the end user's perspective, when the video is transmitted over a bottleneck link. For UDP based streaming, the bottleneck link results in spatial and temporal video artifacts, decreasing the video quality. In contrast, in the case of TCP based streaming, the displayed content itself is not disturbed but playback suffers from stalling due to re-buffering. The results of subjective user studies for both scenarios are analyzed in order to assess the transport protocol influences on Quality of Experience of YouTube. To this end, application-level measurements are conducted for YouTube streaming over a network bottleneck in order to develop models for realistic stalling patterns. Furthermore, mapping functions are derived that accurately describe the relationship between network-level impairments and QoE for both protocols.

    Artikel in einem Journal, einer Zeitung oder einem Magazin

    • Casas, P., Seufert, M., Schatz, R.: YOUQMON: A System for On-line Monitoring of YouTube QoE in Operational 3G Networks. ACM SIGMETRICS Performance Evaluation Review. 41, 44-46 (2013).

    Dissertation oder Abschlussarbeit

    • Wehner, N.: Unsupervised QoE Field Study for Mobile YouTube Video Streaming with YoMoApp, (2017).
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