Zusammenfassung
Global Internet video traffic will dramatically increase in the next years. With this rapid growth, interest in the application behavior of video streaming services and the resulting user experience rises. In particular, there is a need to understand the influence of system parameters on the streaming performance. Thus, several monitoring approaches have been developed which allow conducting automated measurements on a large-scale, for example, lightweight approaches for measurements running in the mobile networks or setups using a virtual frame buffer for servers without a display running in virtualized cloud environments. In some cases, the measurement hardware can totally be controlled, in other cases, there is no knowledge about parallel running services. In this paper, we answer the question whether and how much the measurement setup (e.g., virtualization, headless browsers, load on machines) influences the video streaming behavior. Therefore, we compare eight management setups with the ground truth (an end user watching a video on the own device) by evaluating the key performance indicators on application layer during video streaming, i.e. initial video playout delays and stalling. Our results reveal important insights: some of the common measurement setups heavily influence the measurements and must be avoided to collect reliable results.
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