The widespread deployment and adoption of the Dynamic Adaptive Streaming over HTTP (DASH) standard is making Internet video-on-demand a 'standard' Internet application similar in impact as email and web browsing. While video streaming has been widely deployed and studied for decades, DASH-based streaming is very different as it involves adaptation both by the application and by TCP. The dynamics and implications of multiple levels of end-to-end congestion control are not well understood. The contribution of the research presented in this paper is twofold: first, we characterize the bandwidth consumption of a widely deployed DASH application (i.e., Netflix); second, we provide insight in how different implementations and different access networks can impact bandwidth consumption. Our results suggest that Netflix adaptation defaults to underlying TCP mechanisms during periods of heavy, sustained network congestion. However, the application algorithm is clearly intertwined with the underlying TCP mechanisms during periods of volatile network conditions. In one network scenario, we observed that a backlogged TCP flow achieved a throughput of 6 Mbps while a Netflix session (under similar path conditions) consumed less than 3 Mbps of bandwidth.

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Virtual machine (VM) technology is widely used in higher education to support pedagogy and research. However, without a standard platform for managing VM technology, VM-based infrastructure is likely to be acquired and deployed in an ad-hoc manner. The virtual computing lab (VCL) system is an open source cloud computing platform developed specifically to support higher education. The main goal of VCL is to make available dedicated, custom compute environments to users. We built a small VCL cloud and trialed its use to support several networking courses offered in the School of Computing at Clemson University during the 2013/2014 academic year. In this paper, we summarise the trial and provide results and conclusions. We found that well provisioned PCs might support up to ten VMs however when subject to peak loads, the system is not able to support accurate and reproducible network experiments.

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