{"title":"海报:当你睡觉时-时间偏移预取YouTube视频，以减少高峰时间蜂窝数据的使用","authors":"Shruti Lall, U. Moravapalle, Raghupathy Sivakumar","doi":"10.1145/3349621.3355732","DOIUrl":null,"url":null,"abstract":"The load on wireless cellular networks is not uniformly distributed through the day, and is significantly higher during peak-times. In this context, we present a time-shifted prefetching solution that prefetches content during off-peak periods of network connectivity. We specifically focus on YouTube as it represents a significant portion of overall cellular data-usage. We present a prediction algorithm called MANTIS using a K-nearest neighbor classifier approach and show that the algorithm can reduce the traffic during peak-times by 34% for a typical user.","PeriodicalId":62224,"journal":{"name":"世界中学生文摘","volume":"12 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Poster: While You Were Sleeping- Time-Shifted Prefetching of YouTube Videos to Reduce Peak-time Cellular Data Usage\",\"authors\":\"Shruti Lall, U. Moravapalle, Raghupathy Sivakumar\",\"doi\":\"10.1145/3349621.3355732\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The load on wireless cellular networks is not uniformly distributed through the day, and is significantly higher during peak-times. In this context, we present a time-shifted prefetching solution that prefetches content during off-peak periods of network connectivity. We specifically focus on YouTube as it represents a significant portion of overall cellular data-usage. We present a prediction algorithm called MANTIS using a K-nearest neighbor classifier approach and show that the algorithm can reduce the traffic during peak-times by 34% for a typical user.\",\"PeriodicalId\":62224,\"journal\":{\"name\":\"世界中学生文摘\",\"volume\":\"12 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"世界中学生文摘\",\"FirstCategoryId\":\"90\",\"ListUrlMain\":\"https://doi.org/10.1145/3349621.3355732\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"世界中学生文摘","FirstCategoryId":"90","ListUrlMain":"https://doi.org/10.1145/3349621.3355732","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Poster: While You Were Sleeping- Time-Shifted Prefetching of YouTube Videos to Reduce Peak-time Cellular Data Usage
The load on wireless cellular networks is not uniformly distributed through the day, and is significantly higher during peak-times. In this context, we present a time-shifted prefetching solution that prefetches content during off-peak periods of network connectivity. We specifically focus on YouTube as it represents a significant portion of overall cellular data-usage. We present a prediction algorithm called MANTIS using a K-nearest neighbor classifier approach and show that the algorithm can reduce the traffic during peak-times by 34% for a typical user.
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