YouTube relies on a massively distributed Content Delivery Network (CDN) to stream the billions of videos in its catalogue. Unfortunately, very little information about the design of such CDN is available. This, combined with the pervasiveness of YouTube, poses a big challenge for Internet Service Providers (ISPs), which are compelled to optimize end-users' Quality of Experience (QoE) while having no control on the CDN decisions. This paper presents YouLighter, an unsupervised technique to identify changes in the YouTube CDN. YouLighter leverages only passive measurements to cluster co-located identical caches into edge-nodes. This automatically unveils the structure of YouTube's CDN. Further, we propose a new metric, called Constellation Distance, that compares the clustering obtained from two different time snapshots, to pinpoint sudden changes. While several approaches allow comparison between the clustering results from the same dataset, no technique allows to measure the similarity of clusters from different datasets. Hence, we develop a novel methodology, based on the Constellation Distance, to solve this problem. By running YouLighter over 10-month long traces obtained from two ISPs in different countries, we pinpoint both sudden changes in edge-node allocation, and small alterations to the cache allocation policies which actually impair the QoE that the end-users perceive.
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@cite_9: YouTube has become the most successful Internet website providing a new generation of short video sharing service since its establishment in early 2005. YouTube has a great impact on Internet traffic nowadays, yet itself is suffering from a severe problem of scalability. Therefore, understanding the characteristics of YouTube and similar sites is essential to network traffic engineering and to their sustainable development. To this end, we have crawled the YouTube site for four months, collecting more than 3 million YouTube videos' data. In this paper, we present a systematic and in-depth measurement study on the statistics of YouTube videos. We have found that YouTube videos have noticeably different statistics compared to traditional streaming videos, ranging from length and access pattern, to their growth trend and active life span. We investigate the social networking in YouTube videos, as this is a key driving force toward its success. In particular, we find that the links to related videos generated by uploaders' choices have clear small-world characteristics. This indicates that the videos have strong correlations with each other, and creates opportunities for developing novel techniques to enhance the service quality.
@cite_6: Understanding content popularity growth is of great importance to Internet service providers, content creators and online marketers. In this work, we characterize the growth patterns of video popularity on the currently most popular video sharing application, namely YouTube. Using newly provided data by the application, we analyze how the popularity of individual videos evolves since the video's upload time. Moreover, addressing a key aspect that has been mostly overlooked by previous work, we characterize the types of the referrers that most often attracted users to each video, aiming at shedding some light into the mechanisms (e.g., searching or external linking) that often drive users towards a video, and thus contribute to popularity growth. Our analyses are performed separately for three video datasets, namely, videos that appear in the YouTube top lists, videos removed from the system due to copyright violation, and videos selected according to random queries submitted to YouTube's search engine. Our results show that popularity growth patterns depend on the video dataset. In particular, copyright protected videos tend to get most of their views much earlier in their lifetimes, often exhibiting a popularity growth characterized by a viral epidemic-like propagation process. In contrast, videos in the top lists tend to experience sudden significant bursts of popularity. We also show that not only search but also other YouTube internal mechanisms play important roles to attract users to videos in all three datasets.
@cite_23: This paper presents the results of an investigation into the application flow control technique utilised by YouTube. We reveal and describe the basic properties of YouTube application flow control, which we term block sending, and show that it is widely used by YouTube servers. We also examine how the block sending algorithm interacts with the flow control provided by TCP and reveal that the block sending approach was responsible for over 40 of packet loss events in YouTube flows in a residential DSL dataset and the retransmission of over 1 of all YouTube data sent after the application flow control began. We conclude by suggesting that changing YouTube block sending to be less bursty would improve the performance and reduce the bandwidth usage of YouTube video streams.
@cite_10: Dynamic Adaptive Streaming over HTTP (DASH) is a new streaming standard which adaptively streams video based on the link bandwidth between server and client. DASH encoded videos are chunked in small segments and each segment can have different representations. Switching between these representations enables adaptive streaming, which has the potential to reduce bandwidth consumption in cases where a video is not completely watched. In this paper, we present an analysis on the advantages and disadvantages of using DASH as YouTube's video streaming format. To perform this analysis, we make use of a YouTube video trace and analyze the potential reduction in bandwidth consumption by employing DASH in YouTube, based on user watching patterns. Results from our analysis show that by employing DASH with a segment interval of 2 seconds, we can obtain 95 reduction in bandwidth for low quality videos and up to 83 reduction for HD videos in cases where users do not watch videos completely, which is the case for 42 of all video requests in our trace. Considering all videos requested in the trace the overall bandwidth reduction is 40 for low quality videos and 35 for HD videos.
YouTube had been the subject of study in many papers from different perspectives: from users' behavior @cite_7 to the social network @cite_9 , from video popularity dynamics @cite_6 to protocols aspects @cite_23 @cite_10 .