{"title":"YouTube社交网络中基于兴趣的逐社区P2P短视频共享层次结构","authors":"Haiying Shen, Yuhua Lin, Harrison Chandler","doi":"10.1109/ICDCS.2014.38","DOIUrl":null,"url":null,"abstract":"The past few years have seen an explosion in the popularity of online short-video sharing in You Tube. As the number of users continued to grow, the bandwidth required to maintain acceptable quality of service (QoS) has greatly increased. Peer-to-peer (P2P) architectures have shown promise in reducing the bandwidth costs, however, the previous works build one P2P overlay for each video, which provides limited availability of video providers and produces high overlay maintenance overhead. To handle these problems, in this work, we novelly leverage the existing social network in You Tube, where a user subscribes to another user's channel to track all his uploaded videos. The subscribers of a channel tend to watch the channel's videos and common-interest nodes tend to watch the same videos. Also, the popularity of videos in one channel varies greatly. We study real trace data to confirm these properties. Based on these properties, we propose Social Tube that builds the subscribers of one channel into a P2P overlay and also clusters common-interest nodes in a higher level. It also incorporates a prefetching algorithm that prefetches higher-popularity videos. Extensive trace-driven simulation results and Planet Lab real world experimental results verify the effectiveness of Social Tube at reducing server load and overlay maintenance overhead and at improving QoS for users.","PeriodicalId":170186,"journal":{"name":"2014 IEEE 34th International Conference on Distributed Computing Systems","volume":"118 41","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"An Interest-Based Per-Community P2P Hierarchical Structure for Short Video Sharing in the YouTube Social Network\",\"authors\":\"Haiying Shen, Yuhua Lin, Harrison Chandler\",\"doi\":\"10.1109/ICDCS.2014.38\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The past few years have seen an explosion in the popularity of online short-video sharing in You Tube. As the number of users continued to grow, the bandwidth required to maintain acceptable quality of service (QoS) has greatly increased. Peer-to-peer (P2P) architectures have shown promise in reducing the bandwidth costs, however, the previous works build one P2P overlay for each video, which provides limited availability of video providers and produces high overlay maintenance overhead. To handle these problems, in this work, we novelly leverage the existing social network in You Tube, where a user subscribes to another user's channel to track all his uploaded videos. The subscribers of a channel tend to watch the channel's videos and common-interest nodes tend to watch the same videos. Also, the popularity of videos in one channel varies greatly. We study real trace data to confirm these properties. Based on these properties, we propose Social Tube that builds the subscribers of one channel into a P2P overlay and also clusters common-interest nodes in a higher level. It also incorporates a prefetching algorithm that prefetches higher-popularity videos. Extensive trace-driven simulation results and Planet Lab real world experimental results verify the effectiveness of Social Tube at reducing server load and overlay maintenance overhead and at improving QoS for users.\",\"PeriodicalId\":170186,\"journal\":{\"name\":\"2014 IEEE 34th International Conference on Distributed Computing Systems\",\"volume\":\"118 41\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-06-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE 34th International Conference on Distributed Computing Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDCS.2014.38\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE 34th International Conference on Distributed Computing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDCS.2014.38","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Interest-Based Per-Community P2P Hierarchical Structure for Short Video Sharing in the YouTube Social Network
The past few years have seen an explosion in the popularity of online short-video sharing in You Tube. As the number of users continued to grow, the bandwidth required to maintain acceptable quality of service (QoS) has greatly increased. Peer-to-peer (P2P) architectures have shown promise in reducing the bandwidth costs, however, the previous works build one P2P overlay for each video, which provides limited availability of video providers and produces high overlay maintenance overhead. To handle these problems, in this work, we novelly leverage the existing social network in You Tube, where a user subscribes to another user's channel to track all his uploaded videos. The subscribers of a channel tend to watch the channel's videos and common-interest nodes tend to watch the same videos. Also, the popularity of videos in one channel varies greatly. We study real trace data to confirm these properties. Based on these properties, we propose Social Tube that builds the subscribers of one channel into a P2P overlay and also clusters common-interest nodes in a higher level. It also incorporates a prefetching algorithm that prefetches higher-popularity videos. Extensive trace-driven simulation results and Planet Lab real world experimental results verify the effectiveness of Social Tube at reducing server load and overlay maintenance overhead and at improving QoS for users.