T. Banditwattanawong, Masawee Masdisornchote, P. Uthayopas
{"title":"社交媒体内容的智能分发","authors":"T. Banditwattanawong, Masawee Masdisornchote, P. Uthayopas","doi":"10.1109/IEECON.2014.6925834","DOIUrl":null,"url":null,"abstract":"Today's data such as social media contents and archive of digital contents gathered via ubiquitous devices have been hosted on cloud and being shared in a distribution manner. This causes network link congestions, delayed cloud services and increases in public cloud data-out charges. Simulations have demonstrated that deploying our approach, i-Cloud, as the core mechanism of cloud cache could alleviate these problems up to 17.24% byte-hit and cost-saving, 17.96% delay-saving and 29.33% cache hit outperforming the other well-known approaches. A main finding was that there is no significant performance difference between i-Cloud learning single-user-community patterns and i-Cloud learning cross-user-community patterns of comparable sizes.","PeriodicalId":306512,"journal":{"name":"2014 International Electrical Engineering Congress (iEECON)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"The smart distribution of social media contents\",\"authors\":\"T. Banditwattanawong, Masawee Masdisornchote, P. Uthayopas\",\"doi\":\"10.1109/IEECON.2014.6925834\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Today's data such as social media contents and archive of digital contents gathered via ubiquitous devices have been hosted on cloud and being shared in a distribution manner. This causes network link congestions, delayed cloud services and increases in public cloud data-out charges. Simulations have demonstrated that deploying our approach, i-Cloud, as the core mechanism of cloud cache could alleviate these problems up to 17.24% byte-hit and cost-saving, 17.96% delay-saving and 29.33% cache hit outperforming the other well-known approaches. A main finding was that there is no significant performance difference between i-Cloud learning single-user-community patterns and i-Cloud learning cross-user-community patterns of comparable sizes.\",\"PeriodicalId\":306512,\"journal\":{\"name\":\"2014 International Electrical Engineering Congress (iEECON)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-03-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 International Electrical Engineering Congress (iEECON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IEECON.2014.6925834\",\"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 International Electrical Engineering Congress (iEECON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEECON.2014.6925834","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Today's data such as social media contents and archive of digital contents gathered via ubiquitous devices have been hosted on cloud and being shared in a distribution manner. This causes network link congestions, delayed cloud services and increases in public cloud data-out charges. Simulations have demonstrated that deploying our approach, i-Cloud, as the core mechanism of cloud cache could alleviate these problems up to 17.24% byte-hit and cost-saving, 17.96% delay-saving and 29.33% cache hit outperforming the other well-known approaches. A main finding was that there is no significant performance difference between i-Cloud learning single-user-community patterns and i-Cloud learning cross-user-community patterns of comparable sizes.