A Survey on Content Placement Algorithms for Cloud-Based Content Delivery Networks <s> 1) OSN RELATIONSHIPS <s> Hosting a collection of millions of videos, YouTube offers several features to help users discover the videos of their interest. For example, YouTube provides video search, related video recommendation and front page highlight. The understanding of how these features drive video views is useful for creating a strategy to drive video popularity. In this paper, we perform a measurement study on data sets crawled from YouTube and find that the related video recommendation, which recommends the videos that are related to the video a user is watching, is one of the most important view sources of videos. Despite the fact that the YouTube video search is the number one source of views in aggregation, the related video recommendation is the main source of views for the majority of the videos on YouTube. Furthermore, our results reveal that there is a strong correlation between the view count of a video and the average view count of its top referrer videos. This implies that a video has a higher chance to become popular when it is placed on the related video recommendation lists of popular videos. We also find that the click through rate from a video to its related videos is high and the position of a video in a related video list plays a critical role in the click through rate. Finally, our evaluation of the impact of the related video recommendation system on the diversity of video views indicates that the current recommendation system helps to increase the diversity of video views in aggregation. <s> BIB001 </s> A Survey on Content Placement Algorithms for Cloud-Based Content Delivery Networks <s> 1) OSN RELATIONSHIPS <s> One of the most popular user activities on the Web is watching videos. Services like YouTube, Vimeo, and Hulu host and stream millions of videos, providing content that is on par with TV. While some of this content is popular all over the globe, some videos might be only watched in a confined, local region. In this work we study the relationship between popularity and locality of online YouTube videos. We investigate whether YouTube videos exhibit geographic locality of interest, with views arising from a confined spatial area rather than from a global one. Our analysis is done on a corpus of more than 20 millions YouTube videos, uploaded over one year from different regions. We find that about 50% of the videos have more than 70% of their views in a single region. By relating locality to viralness we show that social sharing generally widens the geographic reach of a video. If, however, a video cannot carry its social impulse over to other means of discovery, it gets stuck in a more confined geographic region. Finally, we analyze how the geographic properties of a video's views evolve on a daily basis during its lifetime, providing new insights on how the geographic reach of a video changes as its popularity peaks and then fades away. Our results demonstrate how, despite the global nature of the Web, online video consumption appears constrained by geographic locality of interest: this has a potential impact on a wide range of systems and applications, spanning from delivery networks to recommendation and discovery engines, providing new directions for future research. <s> BIB002
It is non-trivial to study and quantify the complex OSN relationships and their effect on CP BIB002 and the influence of spreading BIB001 in social networks on content placement. It is especially hard to understand, if the popularity of UGV rises, how social sharing becomes less important BIB002 when the contrary makes more sense. A research challenge would be to find the referrer that can be attributed to voluntary lookup by users due to popularity from word-ofmouth. Can it also be attributed to UGV being listed in the ''trending'' or ''recommended'' category? In either of these cases, end-user would arrive at YouTube video without social sharing referral. It may be recommended/trending, therefore views and popularity increase just by navigation rather than sharing. The reason is that social sharing pertains to links that are specifically selected by end-users on a social networking website, whereas non-social UGV are typically referred from the ''recommended'', ''related'' or ''trending'' lists BIB002 . YouTube recommendation system is a very close second to YouTube search engine for the top sources for driving view counts BIB001 and there is a strong correlation in popularity of the videos listed under the recommended/related video list and the popularity of the video on the watch page. Consider a video v that has a video r listed in its recommended/related video list, then v is the referrer video and r is the related video. There is a strong correlation in the view count of r with the average view count of its top referrer BIB001 . Therefore, video v has a high probability of becoming popular if it is on the recommended/related list of a popular video r. However, the position of the recommended video on the recommended list is also vital such that recommended videos on the top have approximately 40% probability of being selected by end-users BIB001 . That is, the click through rate BIB001 is high for the items listed higher on the recommended video list. These features affect and are in-turn affected by the popularity of the content they host, which pose unique requirements on the CP algorithms for CCDNs. The recommendation systems and search engines are both top sources for driving view counts BIB001 . Therefore, this insight can be used to build prediction models for CP algorithms that preemptively host content, since it can be predicted with a high probability that related videos will be clicked. It should be noted that the position on the recommended list is also vital and a top related video has up to 41.6% chance of being clicked BIB001 . That is the click through rate is high for items listed higher on the recommended video list. Therefore, the challenge lies in leveraging the recommendation systems to build CP algorithms that can move UGV content based on the recommendations. Also, push-based CP algorithms have a greater chance at success over pullbased in this regard, since caching cannot update content as fast as push-based schemes. A ''group of referrer video for a certain video is a good estimate and indicator of view count of video'' BIB001 . This can be leveraged in CP algorithms, by predicting the UGV content that has a high probability of being requested based on the high click-through rate, investigated in BIB001 .