{"title":"以用户为中心的云计算计费模型","authors":"R. Anand Kumar, R. Mittal","doi":"10.1109/ICCCTAM.2012.6488086","DOIUrl":null,"url":null,"abstract":"This paper proposed a more user centered billing model that allows the user to make an informed decision on selecting from a set of cloud offerings or between offerings from different clouds. An architecture for Infrastructure as a Service for such a billing model is presented. This billing model requires the estimation of costs for various configurations of computational requirement. Such a scenario would require estimation of application execution time. This paper evaluates the Worst Case Execution Time techniques available for real time systems for the cloud computing context. A neural network based application execution time based on non-mission critical systems, user specified parameters (e.g., CPU, operating system, and database parameters), historical training data and limited simulations is presented here.","PeriodicalId":111485,"journal":{"name":"2012 International Conference on Cloud Computing Technologies, Applications and Management (ICCCTAM)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"An user-centric billing model for cloud computing\",\"authors\":\"R. Anand Kumar, R. Mittal\",\"doi\":\"10.1109/ICCCTAM.2012.6488086\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposed a more user centered billing model that allows the user to make an informed decision on selecting from a set of cloud offerings or between offerings from different clouds. An architecture for Infrastructure as a Service for such a billing model is presented. This billing model requires the estimation of costs for various configurations of computational requirement. Such a scenario would require estimation of application execution time. This paper evaluates the Worst Case Execution Time techniques available for real time systems for the cloud computing context. A neural network based application execution time based on non-mission critical systems, user specified parameters (e.g., CPU, operating system, and database parameters), historical training data and limited simulations is presented here.\",\"PeriodicalId\":111485,\"journal\":{\"name\":\"2012 International Conference on Cloud Computing Technologies, Applications and Management (ICCCTAM)\",\"volume\":\"34 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 International Conference on Cloud Computing Technologies, Applications and Management (ICCCTAM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCCTAM.2012.6488086\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 International Conference on Cloud Computing Technologies, Applications and Management (ICCCTAM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCCTAM.2012.6488086","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper proposed a more user centered billing model that allows the user to make an informed decision on selecting from a set of cloud offerings or between offerings from different clouds. An architecture for Infrastructure as a Service for such a billing model is presented. This billing model requires the estimation of costs for various configurations of computational requirement. Such a scenario would require estimation of application execution time. This paper evaluates the Worst Case Execution Time techniques available for real time systems for the cloud computing context. A neural network based application execution time based on non-mission critical systems, user specified parameters (e.g., CPU, operating system, and database parameters), historical training data and limited simulations is presented here.