{"title":"阐明数据驱动cdn的隐藏挑战","authors":"Theophilus A. Benson","doi":"10.1145/3578356.3592574","DOIUrl":null,"url":null,"abstract":"While Data-driven CDNs have the potential to provide unparalleled performance and availability improvements, they open up an intricate and exciting tapestry of previously un-addressed problems. This paper highlights these problems, explores existing solutions, and identifies open research questions for each direction. We, also, present a strawman approach, Guard-Rails, that embodies preliminary techniques that can be used to help safeguard data-driven CDNs against the identified perils.","PeriodicalId":370204,"journal":{"name":"Proceedings of the 3rd Workshop on Machine Learning and Systems","volume":"107 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Illuminating the hidden challenges of data-driven CDNs\",\"authors\":\"Theophilus A. Benson\",\"doi\":\"10.1145/3578356.3592574\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"While Data-driven CDNs have the potential to provide unparalleled performance and availability improvements, they open up an intricate and exciting tapestry of previously un-addressed problems. This paper highlights these problems, explores existing solutions, and identifies open research questions for each direction. We, also, present a strawman approach, Guard-Rails, that embodies preliminary techniques that can be used to help safeguard data-driven CDNs against the identified perils.\",\"PeriodicalId\":370204,\"journal\":{\"name\":\"Proceedings of the 3rd Workshop on Machine Learning and Systems\",\"volume\":\"107 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 3rd Workshop on Machine Learning and Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3578356.3592574\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 3rd Workshop on Machine Learning and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3578356.3592574","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Illuminating the hidden challenges of data-driven CDNs
While Data-driven CDNs have the potential to provide unparalleled performance and availability improvements, they open up an intricate and exciting tapestry of previously un-addressed problems. This paper highlights these problems, explores existing solutions, and identifies open research questions for each direction. We, also, present a strawman approach, Guard-Rails, that embodies preliminary techniques that can be used to help safeguard data-driven CDNs against the identified perils.
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