{"title":"智能MapReduce云:根据需要对中间数据应用额外的处理","authors":"Tzu-Chi Huang, Kuo-Chih Chu, Ming-Fong Tsai","doi":"10.1109/PADSW.2014.7097885","DOIUrl":null,"url":null,"abstract":"Cloud computing is the emerging and attractive technology and provides users with various services in a pay-as-you-go manner. Cloud computing nowadays does not limit resources of the services in a cloud to the computers that are far away from users and connected to each other in a data center with high speed networks at the same geographic location. Cloud computing may present a cloud to users by connecting resources at multiple geographic locations. By connecting resources at multiple geographic locations to organize a cloud, cloud computing may meet problems of communication interception, congestion, and interruption. Cloud computing should have a way to supply extra processing on demand for certain links between computers separated geographically. Since a MapReduce cloud is the key to the success of the large-scale computation, cloud computing can use the Smart MapReduce Cloud (SMRC) proposed in this paper to apply extra processing to intermediate data on demand while intermediate data is delivered among computers in the MapReduce cloud. In experiments, cloud computing is tested with several popular MapReduce applications to observe performances of data encryption and compression via XOR and GZIP functions in SMRC.","PeriodicalId":421740,"journal":{"name":"2014 20th IEEE International Conference on Parallel and Distributed Systems (ICPADS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Smart MapReduce cloud: Applying extra processing to intermediate data on demand\",\"authors\":\"Tzu-Chi Huang, Kuo-Chih Chu, Ming-Fong Tsai\",\"doi\":\"10.1109/PADSW.2014.7097885\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Cloud computing is the emerging and attractive technology and provides users with various services in a pay-as-you-go manner. Cloud computing nowadays does not limit resources of the services in a cloud to the computers that are far away from users and connected to each other in a data center with high speed networks at the same geographic location. Cloud computing may present a cloud to users by connecting resources at multiple geographic locations. By connecting resources at multiple geographic locations to organize a cloud, cloud computing may meet problems of communication interception, congestion, and interruption. Cloud computing should have a way to supply extra processing on demand for certain links between computers separated geographically. Since a MapReduce cloud is the key to the success of the large-scale computation, cloud computing can use the Smart MapReduce Cloud (SMRC) proposed in this paper to apply extra processing to intermediate data on demand while intermediate data is delivered among computers in the MapReduce cloud. In experiments, cloud computing is tested with several popular MapReduce applications to observe performances of data encryption and compression via XOR and GZIP functions in SMRC.\",\"PeriodicalId\":421740,\"journal\":{\"name\":\"2014 20th IEEE International Conference on Parallel and Distributed Systems (ICPADS)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 20th IEEE International Conference on Parallel and Distributed Systems (ICPADS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PADSW.2014.7097885\",\"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 20th IEEE International Conference on Parallel and Distributed Systems (ICPADS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PADSW.2014.7097885","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Smart MapReduce cloud: Applying extra processing to intermediate data on demand
Cloud computing is the emerging and attractive technology and provides users with various services in a pay-as-you-go manner. Cloud computing nowadays does not limit resources of the services in a cloud to the computers that are far away from users and connected to each other in a data center with high speed networks at the same geographic location. Cloud computing may present a cloud to users by connecting resources at multiple geographic locations. By connecting resources at multiple geographic locations to organize a cloud, cloud computing may meet problems of communication interception, congestion, and interruption. Cloud computing should have a way to supply extra processing on demand for certain links between computers separated geographically. Since a MapReduce cloud is the key to the success of the large-scale computation, cloud computing can use the Smart MapReduce Cloud (SMRC) proposed in this paper to apply extra processing to intermediate data on demand while intermediate data is delivered among computers in the MapReduce cloud. In experiments, cloud computing is tested with several popular MapReduce applications to observe performances of data encryption and compression via XOR and GZIP functions in SMRC.