A Survey on Content Placement Algorithms for Cloud-Based Content Delivery Networks <s> C. END-USER BEHAVIOR <s> In this paper we present a complete measurement study that compares YouTube traffic generated by mobile devices (smart-phones,tablets) with traffic generated by common PCs (desktops, notebooks, netbooks). We investigate the users' behavior and correlate it with the system performance. Our measurements are performed using unique data sets which are collected from vantage points in nation-wide ISPs and University campuses from two countries in Europe and the U.S. Our results show that the user access patterns are similar across a wide range of user locations, access technologies and user devices. Users stick with default player configurations, e.g., not changing video resolution or rarely enabling full screen playback. Furthermore it is very common that users abort video playback, with 60% of videos watched for no more than 20% of their duration. We show that the YouTube system is highly optimized for PC access and leverages aggressive buffering policies to guarantee excellent video playback. This however causes 25%-39% of data to be unnecessarily transferred, since users abort the playback very early. This waste of data transferred is even higher when mobile devices are considered. The limited storage offered by those devices makes the video download more complicated and overall less efficient, so that clients typically download more data than the actual video size. Overall, this result calls for better system optimization for both, PC and mobile accesses. <s> BIB001 </s> A Survey on Content Placement Algorithms for Cloud-Based Content Delivery Networks <s> C. END-USER BEHAVIOR <s> Popular online video-on-demand (VoD) services all maintain a large catalog of videos for their users to access. The knowledge of video popularity is very important for system operation , such as video caching on content distribution network (CDN) servers. The video popularity distribution at a given time is quite well understood. We study how the video popularity changes with time, for different types of videos, and apply the results to design video caching strategies. Our study is based on analyzing the video access levels over time, based on data provided by a large video service provider. Our main finding is, while there are variations, the glory days of a video’s popularity typically pass by quickly and the probability of replaying a video by the same user is low. The reason appears to be due to fairly regular number of users and view time per day for each user, and continuous arrival of new videos. All these facts will affect how video popularity changes, hence also affect the optimal video caching strategy. Based on the observation from our measurement study, we propose a mixed replication strategy (of LFU and FIFO) that can handle different kinds of videos. Offline strategy assuming tomorrow’s video popularity is known in advance is used as a performance benchmark. Through trace-driven simulation, we show that the caching performance achieved by the mixed strategy is very close to the performance achieved by the offline strategy. <s> BIB002
The evaluation reveals that there are various challenges in studying end-user behaviors and their effect on CP algorithms. For instance, can CP algorithm designers leverage the short watch time BIB001 of end-users to predict the formats and chunks to host across datacenters? Can it be quantified how rarely the cold videos are viewed BIB002 and deduce a threshold on view count that can be employed to measure when content VOLUME 6, 2018 is cold to leverage it in the design of CP algorithms? Endusers who are subscribed to a content service or are just guest end-users behave differently BIB002 . It remains a challenge to deduce metrics, such as ratio of online active and inactive end-users to adapt or infer CP algorithm characteristics. Since there is a low probability of replay BIB002 , adding metadata to the recorded statistics and information about where the enduser would leave off in the last playback of a certain video should be considered.