{"title":"技术观点:DFI:高速网络的数据流接口","authors":"G. Alonso","doi":"10.1145/3542700.3542704","DOIUrl":null,"url":null,"abstract":"Optimizing data movement has always been one of the key ways to get a data processing system to perform efficiently. Appearing under different disguises as computers evolved over the years, the issue is today as relevant as ever. With the advent of the cloud, data movement has become the bottleneck to address in any data processing system. In the cloud, compute and storage are typically disaggregated, with a network in between. In addition, cloud systems are scale-out, i.e., performance is obtained by parallelizing across machines, which also involves network communication. And while it is possible to use machines with large amounts of memory, the pricing models and the virtualized nature of the cloud tends to favor clusters of smaller computing nodes. Nowadays, the problem of optimizing data movement has become the problem of using the network as efficiently as possible.","PeriodicalId":346332,"journal":{"name":"ACM SIGMOD Record","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Technical perspective: DFI: The Data Flow Interface for High-Speed Networks\",\"authors\":\"G. Alonso\",\"doi\":\"10.1145/3542700.3542704\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Optimizing data movement has always been one of the key ways to get a data processing system to perform efficiently. Appearing under different disguises as computers evolved over the years, the issue is today as relevant as ever. With the advent of the cloud, data movement has become the bottleneck to address in any data processing system. In the cloud, compute and storage are typically disaggregated, with a network in between. In addition, cloud systems are scale-out, i.e., performance is obtained by parallelizing across machines, which also involves network communication. And while it is possible to use machines with large amounts of memory, the pricing models and the virtualized nature of the cloud tends to favor clusters of smaller computing nodes. Nowadays, the problem of optimizing data movement has become the problem of using the network as efficiently as possible.\",\"PeriodicalId\":346332,\"journal\":{\"name\":\"ACM SIGMOD Record\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM SIGMOD Record\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3542700.3542704\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM SIGMOD Record","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3542700.3542704","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Technical perspective: DFI: The Data Flow Interface for High-Speed Networks
Optimizing data movement has always been one of the key ways to get a data processing system to perform efficiently. Appearing under different disguises as computers evolved over the years, the issue is today as relevant as ever. With the advent of the cloud, data movement has become the bottleneck to address in any data processing system. In the cloud, compute and storage are typically disaggregated, with a network in between. In addition, cloud systems are scale-out, i.e., performance is obtained by parallelizing across machines, which also involves network communication. And while it is possible to use machines with large amounts of memory, the pricing models and the virtualized nature of the cloud tends to favor clusters of smaller computing nodes. Nowadays, the problem of optimizing data movement has become the problem of using the network as efficiently as possible.