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Abstract-In this paper, we analyze the YouTube service and the traffic generated from its usage. The purpose of this study is to identify by strictly using passive measurements the information that can be used as metrics or indicators of the progress of individual video sessions and to estimate the impact of these metrics in the user experience. We find a novel method to track the progress of the video playback that, in contrast to previous works, does not require instrumentation of the video player neither browser-based plug-ins. Instead, we extract important statistical information about the status of the playback by reverse engineering the metrics in related HTTP requests that are generated during playback. For the purpose of collecting these metrics, a tool was developed to perform YouTube traffic measurements by means of passive network monitoring in a large university campus network. The analysis of the obtained data revealed the most important sources of initial delay in the sessions as well as buffer outage events and download rate statistics. Further analysis revealed the impact of video advertisements and re-buffering events on the user experience in terms of video abandonment rate.

Abstract. One-way packet delay is an important network performance metric. Recently, a new data structure called Lossy Difference Aggregator (LDA) has been proposed to estimate this metric more efficiently than with the classical approaches of sending individual packet timestamps or probe traffic. This work presents an independent validation of the LDA algorithm and provides an improved analysis that results in a 20% increase in the number of packet delay samples collected by the algorithm. We also extend the analysis by relating the number of collected samples to the accuracy of the LDA and provide additional insight on how to parametrize it. Finally, we extend the algorithm to overcome some of its practical limitations and validate our analysis using real network traffic.

Abstract. Counting the number of flows present in network traffic is not trivial, given that the naive approach of using a hash table to track the active flows is too slow for the current backbone network speeds. Several algorithms have been proposed in the recent literature that can calculate an approximate count using small amount of memory and few memory accesses per packet. Fewer works have addressed the more complex problem of counting flows over sliding windows, where the main challenge is to continuously expire old information. One of the existing proposals is a straightforward adaptation of the direct bitmaps technique to the sliding window model. We present an algorithm called Countdown Vector that also builds upon the direct bitmaps technique. Our algorithm, however, obtains significant cost reductions both in terms of memory and CPU, by introducing an extra approximation in the mechanism in charge of the expiration of old information.

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